{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Home Credit Default Risk, Baseline Model\n",
    "## 1. Data Preparation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('max_columns', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "application = pd.read_csv('all/application_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>NAME_TYPE_SUITE</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>FLAG_EMP_PHONE</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_CONT_MOBILE</th>\n",
       "      <th>FLAG_PHONE</th>\n",
       "      <th>FLAG_EMAIL</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>REGION_RATING_CLIENT</th>\n",
       "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>REG_REGION_NOT_LIVE_REGION</th>\n",
       "      <th>REG_REGION_NOT_WORK_REGION</th>\n",
       "      <th>LIVE_REGION_NOT_WORK_REGION</th>\n",
       "      <th>REG_CITY_NOT_LIVE_CITY</th>\n",
       "      <th>REG_CITY_NOT_WORK_CITY</th>\n",
       "      <th>LIVE_CITY_NOT_WORK_CITY</th>\n",
       "      <th>ORGANIZATION_TYPE</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>APARTMENTS_AVG</th>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
       "      <th>YEARS_BUILD_AVG</th>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <th>ELEVATORS_AVG</th>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <th>FLOORSMAX_AVG</th>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <th>LIVINGAREA_AVG</th>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <th>APARTMENTS_MODE</th>\n",
       "      <th>BASEMENTAREA_MODE</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n",
       "      <th>YEARS_BUILD_MODE</th>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <th>ELEVATORS_MODE</th>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <th>FLOORSMIN_MODE</th>\n",
       "      <th>LANDAREA_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAREA_MODE</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>APARTMENTS_MEDI</th>\n",
       "      <th>BASEMENTAREA_MEDI</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>YEARS_BUILD_MEDI</th>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <th>ELEVATORS_MEDI</th>\n",
       "      <th>ENTRANCES_MEDI</th>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <th>FLOORSMIN_MEDI</th>\n",
       "      <th>LANDAREA_MEDI</th>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <th>HOUSETYPE_MODE</th>\n",
       "      <th>TOTALAREA_MODE</th>\n",
       "      <th>WALLSMATERIAL_MODE</th>\n",
       "      <th>EMERGENCYSTATE_MODE</th>\n",
       "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>FLAG_DOCUMENT_2</th>\n",
       "      <th>FLAG_DOCUMENT_3</th>\n",
       "      <th>FLAG_DOCUMENT_4</th>\n",
       "      <th>FLAG_DOCUMENT_5</th>\n",
       "      <th>FLAG_DOCUMENT_6</th>\n",
       "      <th>FLAG_DOCUMENT_7</th>\n",
       "      <th>FLAG_DOCUMENT_8</th>\n",
       "      <th>FLAG_DOCUMENT_9</th>\n",
       "      <th>FLAG_DOCUMENT_10</th>\n",
       "      <th>FLAG_DOCUMENT_11</th>\n",
       "      <th>FLAG_DOCUMENT_12</th>\n",
       "      <th>FLAG_DOCUMENT_13</th>\n",
       "      <th>FLAG_DOCUMENT_14</th>\n",
       "      <th>FLAG_DOCUMENT_15</th>\n",
       "      <th>FLAG_DOCUMENT_16</th>\n",
       "      <th>FLAG_DOCUMENT_17</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>351000.0</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.018801</td>\n",
       "      <td>-9461</td>\n",
       "      <td>-637</td>\n",
       "      <td>-3648.0</td>\n",
       "      <td>-2120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>WEDNESDAY</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Business Entity Type 3</td>\n",
       "      <td>0.083037</td>\n",
       "      <td>0.262949</td>\n",
       "      <td>0.139376</td>\n",
       "      <td>0.0247</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.9722</td>\n",
       "      <td>0.6192</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.0833</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0252</td>\n",
       "      <td>0.0383</td>\n",
       "      <td>0.9722</td>\n",
       "      <td>0.6341</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.0833</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.0377</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.9722</td>\n",
       "      <td>0.6243</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.0833</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>reg oper account</td>\n",
       "      <td>block of flats</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>Stone, brick</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1134.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>Family</td>\n",
       "      <td>State servant</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>-16765</td>\n",
       "      <td>-1188</td>\n",
       "      <td>-1186.0</td>\n",
       "      <td>-291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>MONDAY</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>School</td>\n",
       "      <td>0.311267</td>\n",
       "      <td>0.622246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.0529</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.7960</td>\n",
       "      <td>0.0605</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.2917</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0773</td>\n",
       "      <td>0.0549</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0924</td>\n",
       "      <td>0.0538</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.8040</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.2917</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0968</td>\n",
       "      <td>0.0529</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.7987</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.2917</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0787</td>\n",
       "      <td>0.0558</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.01</td>\n",
       "      <td>reg oper account</td>\n",
       "      <td>block of flats</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>Block</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-828.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>-19046</td>\n",
       "      <td>-225</td>\n",
       "      <td>-4260.0</td>\n",
       "      <td>-2531</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>MONDAY</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Government</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.555912</td>\n",
       "      <td>0.729567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-815.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>297000.0</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.008019</td>\n",
       "      <td>-19005</td>\n",
       "      <td>-3039</td>\n",
       "      <td>-9833.0</td>\n",
       "      <td>-2437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>WEDNESDAY</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Business Entity Type 3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.650442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-617.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-19932</td>\n",
       "      <td>-3038</td>\n",
       "      <td>-4311.0</td>\n",
       "      <td>-3458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>THURSDAY</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Religion</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.322738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1106.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   AMT_GOODS_PRICE NAME_TYPE_SUITE NAME_INCOME_TYPE  \\\n",
       "0         351000.0   Unaccompanied          Working   \n",
       "1        1129500.0          Family    State servant   \n",
       "2         135000.0   Unaccompanied          Working   \n",
       "3         297000.0   Unaccompanied          Working   \n",
       "4         513000.0   Unaccompanied          Working   \n",
       "\n",
       "             NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  \\\n",
       "0  Secondary / secondary special  Single / not married  House / apartment   \n",
       "1               Higher education               Married  House / apartment   \n",
       "2  Secondary / secondary special  Single / not married  House / apartment   \n",
       "3  Secondary / secondary special        Civil marriage  House / apartment   \n",
       "4  Secondary / secondary special  Single / not married  House / apartment   \n",
       "\n",
       "   REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  \\\n",
       "0                    0.018801       -9461           -637            -3648.0   \n",
       "1                    0.003541      -16765          -1188            -1186.0   \n",
       "2                    0.010032      -19046           -225            -4260.0   \n",
       "3                    0.008019      -19005          -3039            -9833.0   \n",
       "4                    0.028663      -19932          -3038            -4311.0   \n",
       "\n",
       "   DAYS_ID_PUBLISH  OWN_CAR_AGE  FLAG_MOBIL  FLAG_EMP_PHONE  FLAG_WORK_PHONE  \\\n",
       "0            -2120          NaN           1               1                0   \n",
       "1             -291          NaN           1               1                0   \n",
       "2            -2531         26.0           1               1                1   \n",
       "3            -2437          NaN           1               1                0   \n",
       "4            -3458          NaN           1               1                0   \n",
       "\n",
       "   FLAG_CONT_MOBILE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  CNT_FAM_MEMBERS  \\\n",
       "0                 1           1           0        Laborers              1.0   \n",
       "1                 1           1           0      Core staff              2.0   \n",
       "2                 1           1           0        Laborers              1.0   \n",
       "3                 1           0           0        Laborers              2.0   \n",
       "4                 1           0           0      Core staff              1.0   \n",
       "\n",
       "   REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY  \\\n",
       "0                     2                            2   \n",
       "1                     1                            1   \n",
       "2                     2                            2   \n",
       "3                     2                            2   \n",
       "4                     2                            2   \n",
       "\n",
       "  WEEKDAY_APPR_PROCESS_START  HOUR_APPR_PROCESS_START  \\\n",
       "0                  WEDNESDAY                       10   \n",
       "1                     MONDAY                       11   \n",
       "2                     MONDAY                        9   \n",
       "3                  WEDNESDAY                       17   \n",
       "4                   THURSDAY                       11   \n",
       "\n",
       "   REG_REGION_NOT_LIVE_REGION  REG_REGION_NOT_WORK_REGION  \\\n",
       "0                           0                           0   \n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           0   \n",
       "\n",
       "   LIVE_REGION_NOT_WORK_REGION  REG_CITY_NOT_LIVE_CITY  \\\n",
       "0                            0                       0   \n",
       "1                            0                       0   \n",
       "2                            0                       0   \n",
       "3                            0                       0   \n",
       "4                            0                       0   \n",
       "\n",
       "   REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY       ORGANIZATION_TYPE  \\\n",
       "0                       0                        0  Business Entity Type 3   \n",
       "1                       0                        0                  School   \n",
       "2                       0                        0              Government   \n",
       "3                       0                        0  Business Entity Type 3   \n",
       "4                       1                        1                Religion   \n",
       "\n",
       "   EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  APARTMENTS_AVG  BASEMENTAREA_AVG  \\\n",
       "0      0.083037      0.262949      0.139376          0.0247            0.0369   \n",
       "1      0.311267      0.622246           NaN          0.0959            0.0529   \n",
       "2           NaN      0.555912      0.729567             NaN               NaN   \n",
       "3           NaN      0.650442           NaN             NaN               NaN   \n",
       "4           NaN      0.322738           NaN             NaN               NaN   \n",
       "\n",
       "   YEARS_BEGINEXPLUATATION_AVG  YEARS_BUILD_AVG  COMMONAREA_AVG  \\\n",
       "0                       0.9722           0.6192          0.0143   \n",
       "1                       0.9851           0.7960          0.0605   \n",
       "2                          NaN              NaN             NaN   \n",
       "3                          NaN              NaN             NaN   \n",
       "4                          NaN              NaN             NaN   \n",
       "\n",
       "   ELEVATORS_AVG  ENTRANCES_AVG  FLOORSMAX_AVG  FLOORSMIN_AVG  LANDAREA_AVG  \\\n",
       "0           0.00         0.0690         0.0833         0.1250        0.0369   \n",
       "1           0.08         0.0345         0.2917         0.3333        0.0130   \n",
       "2            NaN            NaN            NaN            NaN           NaN   \n",
       "3            NaN            NaN            NaN            NaN           NaN   \n",
       "4            NaN            NaN            NaN            NaN           NaN   \n",
       "\n",
       "   LIVINGAPARTMENTS_AVG  LIVINGAREA_AVG  NONLIVINGAPARTMENTS_AVG  \\\n",
       "0                0.0202          0.0190                   0.0000   \n",
       "1                0.0773          0.0549                   0.0039   \n",
       "2                   NaN             NaN                      NaN   \n",
       "3                   NaN             NaN                      NaN   \n",
       "4                   NaN             NaN                      NaN   \n",
       "\n",
       "   NONLIVINGAREA_AVG  APARTMENTS_MODE  BASEMENTAREA_MODE  \\\n",
       "0             0.0000           0.0252             0.0383   \n",
       "1             0.0098           0.0924             0.0538   \n",
       "2                NaN              NaN                NaN   \n",
       "3                NaN              NaN                NaN   \n",
       "4                NaN              NaN                NaN   \n",
       "\n",
       "   YEARS_BEGINEXPLUATATION_MODE  YEARS_BUILD_MODE  COMMONAREA_MODE  \\\n",
       "0                        0.9722            0.6341           0.0144   \n",
       "1                        0.9851            0.8040           0.0497   \n",
       "2                           NaN               NaN              NaN   \n",
       "3                           NaN               NaN              NaN   \n",
       "4                           NaN               NaN              NaN   \n",
       "\n",
       "   ELEVATORS_MODE  ENTRANCES_MODE  FLOORSMAX_MODE  FLOORSMIN_MODE  \\\n",
       "0          0.0000          0.0690          0.0833          0.1250   \n",
       "1          0.0806          0.0345          0.2917          0.3333   \n",
       "2             NaN             NaN             NaN             NaN   \n",
       "3             NaN             NaN             NaN             NaN   \n",
       "4             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   LANDAREA_MODE  LIVINGAPARTMENTS_MODE  LIVINGAREA_MODE  \\\n",
       "0         0.0377                  0.022           0.0198   \n",
       "1         0.0128                  0.079           0.0554   \n",
       "2            NaN                    NaN              NaN   \n",
       "3            NaN                    NaN              NaN   \n",
       "4            NaN                    NaN              NaN   \n",
       "\n",
       "   NONLIVINGAPARTMENTS_MODE  NONLIVINGAREA_MODE  APARTMENTS_MEDI  \\\n",
       "0                       0.0                 0.0           0.0250   \n",
       "1                       0.0                 0.0           0.0968   \n",
       "2                       NaN                 NaN              NaN   \n",
       "3                       NaN                 NaN              NaN   \n",
       "4                       NaN                 NaN              NaN   \n",
       "\n",
       "   BASEMENTAREA_MEDI  YEARS_BEGINEXPLUATATION_MEDI  YEARS_BUILD_MEDI  \\\n",
       "0             0.0369                        0.9722            0.6243   \n",
       "1             0.0529                        0.9851            0.7987   \n",
       "2                NaN                           NaN               NaN   \n",
       "3                NaN                           NaN               NaN   \n",
       "4                NaN                           NaN               NaN   \n",
       "\n",
       "   COMMONAREA_MEDI  ELEVATORS_MEDI  ENTRANCES_MEDI  FLOORSMAX_MEDI  \\\n",
       "0           0.0144            0.00          0.0690          0.0833   \n",
       "1           0.0608            0.08          0.0345          0.2917   \n",
       "2              NaN             NaN             NaN             NaN   \n",
       "3              NaN             NaN             NaN             NaN   \n",
       "4              NaN             NaN             NaN             NaN   \n",
       "\n",
       "   FLOORSMIN_MEDI  LANDAREA_MEDI  LIVINGAPARTMENTS_MEDI  LIVINGAREA_MEDI  \\\n",
       "0          0.1250         0.0375                 0.0205           0.0193   \n",
       "1          0.3333         0.0132                 0.0787           0.0558   \n",
       "2             NaN            NaN                    NaN              NaN   \n",
       "3             NaN            NaN                    NaN              NaN   \n",
       "4             NaN            NaN                    NaN              NaN   \n",
       "\n",
       "   NONLIVINGAPARTMENTS_MEDI  NONLIVINGAREA_MEDI FONDKAPREMONT_MODE  \\\n",
       "0                    0.0000                0.00   reg oper account   \n",
       "1                    0.0039                0.01   reg oper account   \n",
       "2                       NaN                 NaN                NaN   \n",
       "3                       NaN                 NaN                NaN   \n",
       "4                       NaN                 NaN                NaN   \n",
       "\n",
       "   HOUSETYPE_MODE  TOTALAREA_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE  \\\n",
       "0  block of flats          0.0149       Stone, brick                  No   \n",
       "1  block of flats          0.0714              Block                  No   \n",
       "2             NaN             NaN                NaN                 NaN   \n",
       "3             NaN             NaN                NaN                 NaN   \n",
       "4             NaN             NaN                NaN                 NaN   \n",
       "\n",
       "   OBS_30_CNT_SOCIAL_CIRCLE  DEF_30_CNT_SOCIAL_CIRCLE  \\\n",
       "0                       2.0                       2.0   \n",
       "1                       1.0                       0.0   \n",
       "2                       0.0                       0.0   \n",
       "3                       2.0                       0.0   \n",
       "4                       0.0                       0.0   \n",
       "\n",
       "   OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  \\\n",
       "0                       2.0                       2.0                 -1134.0   \n",
       "1                       1.0                       0.0                  -828.0   \n",
       "2                       0.0                       0.0                  -815.0   \n",
       "3                       2.0                       0.0                  -617.0   \n",
       "4                       0.0                       0.0                 -1106.0   \n",
       "\n",
       "   FLAG_DOCUMENT_2  FLAG_DOCUMENT_3  FLAG_DOCUMENT_4  FLAG_DOCUMENT_5  \\\n",
       "0                0                1                0                0   \n",
       "1                0                1                0                0   \n",
       "2                0                0                0                0   \n",
       "3                0                1                0                0   \n",
       "4                0                0                0                0   \n",
       "\n",
       "   FLAG_DOCUMENT_6  FLAG_DOCUMENT_7  FLAG_DOCUMENT_8  FLAG_DOCUMENT_9  \\\n",
       "0                0                0                0                0   \n",
       "1                0                0                0                0   \n",
       "2                0                0                0                0   \n",
       "3                0                0                0                0   \n",
       "4                0                0                1                0   \n",
       "\n",
       "   FLAG_DOCUMENT_10  FLAG_DOCUMENT_11  FLAG_DOCUMENT_12  FLAG_DOCUMENT_13  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  FLAG_DOCUMENT_17  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         NaN                        NaN   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         NaN                        NaN   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "col_categorical = [col for col in application.columns if application[col].dtype == 'object']\n",
    "\n",
    "for col in col_categorical:\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    le.fit(list(application[col].values.astype('str')))\n",
    "    application[col] = le.transform(list(application[col].values.astype('str')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_describe = application.describe()\n",
    "col_missing = summary_describe.columns[summary_describe.loc['count',:] < application.shape[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <td>1.615500e+03</td>\n",
       "      <td>2.580255e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <td>4.050000e+04</td>\n",
       "      <td>4.050000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>1.456813e-02</td>\n",
       "      <td>9.626928e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>8.173617e-08</td>\n",
       "      <td>8.549997e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>5.272652e-04</td>\n",
       "      <td>8.960095e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APARTMENTS_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APARTMENTS_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APARTMENTS_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTRANCES_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTALAREA_MODE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.480000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.440000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <td>-4.292000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.700000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.610000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.500000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       min           max\n",
       "AMT_ANNUITY                   1.615500e+03  2.580255e+05\n",
       "AMT_GOODS_PRICE               4.050000e+04  4.050000e+06\n",
       "OWN_CAR_AGE                   0.000000e+00  9.100000e+01\n",
       "CNT_FAM_MEMBERS               1.000000e+00  2.000000e+01\n",
       "EXT_SOURCE_1                  1.456813e-02  9.626928e-01\n",
       "EXT_SOURCE_2                  8.173617e-08  8.549997e-01\n",
       "EXT_SOURCE_3                  5.272652e-04  8.960095e-01\n",
       "APARTMENTS_AVG                0.000000e+00  1.000000e+00\n",
       "BASEMENTAREA_AVG              0.000000e+00  1.000000e+00\n",
       "YEARS_BEGINEXPLUATATION_AVG   0.000000e+00  1.000000e+00\n",
       "YEARS_BUILD_AVG               0.000000e+00  1.000000e+00\n",
       "COMMONAREA_AVG                0.000000e+00  1.000000e+00\n",
       "ELEVATORS_AVG                 0.000000e+00  1.000000e+00\n",
       "ENTRANCES_AVG                 0.000000e+00  1.000000e+00\n",
       "FLOORSMAX_AVG                 0.000000e+00  1.000000e+00\n",
       "FLOORSMIN_AVG                 0.000000e+00  1.000000e+00\n",
       "LANDAREA_AVG                  0.000000e+00  1.000000e+00\n",
       "LIVINGAPARTMENTS_AVG          0.000000e+00  1.000000e+00\n",
       "LIVINGAREA_AVG                0.000000e+00  1.000000e+00\n",
       "NONLIVINGAPARTMENTS_AVG       0.000000e+00  1.000000e+00\n",
       "NONLIVINGAREA_AVG             0.000000e+00  1.000000e+00\n",
       "APARTMENTS_MODE               0.000000e+00  1.000000e+00\n",
       "BASEMENTAREA_MODE             0.000000e+00  1.000000e+00\n",
       "YEARS_BEGINEXPLUATATION_MODE  0.000000e+00  1.000000e+00\n",
       "YEARS_BUILD_MODE              0.000000e+00  1.000000e+00\n",
       "COMMONAREA_MODE               0.000000e+00  1.000000e+00\n",
       "ELEVATORS_MODE                0.000000e+00  1.000000e+00\n",
       "ENTRANCES_MODE                0.000000e+00  1.000000e+00\n",
       "FLOORSMAX_MODE                0.000000e+00  1.000000e+00\n",
       "FLOORSMIN_MODE                0.000000e+00  1.000000e+00\n",
       "...                                    ...           ...\n",
       "LIVINGAPARTMENTS_MODE         0.000000e+00  1.000000e+00\n",
       "LIVINGAREA_MODE               0.000000e+00  1.000000e+00\n",
       "NONLIVINGAPARTMENTS_MODE      0.000000e+00  1.000000e+00\n",
       "NONLIVINGAREA_MODE            0.000000e+00  1.000000e+00\n",
       "APARTMENTS_MEDI               0.000000e+00  1.000000e+00\n",
       "BASEMENTAREA_MEDI             0.000000e+00  1.000000e+00\n",
       "YEARS_BEGINEXPLUATATION_MEDI  0.000000e+00  1.000000e+00\n",
       "YEARS_BUILD_MEDI              0.000000e+00  1.000000e+00\n",
       "COMMONAREA_MEDI               0.000000e+00  1.000000e+00\n",
       "ELEVATORS_MEDI                0.000000e+00  1.000000e+00\n",
       "ENTRANCES_MEDI                0.000000e+00  1.000000e+00\n",
       "FLOORSMAX_MEDI                0.000000e+00  1.000000e+00\n",
       "FLOORSMIN_MEDI                0.000000e+00  1.000000e+00\n",
       "LANDAREA_MEDI                 0.000000e+00  1.000000e+00\n",
       "LIVINGAPARTMENTS_MEDI         0.000000e+00  1.000000e+00\n",
       "LIVINGAREA_MEDI               0.000000e+00  1.000000e+00\n",
       "NONLIVINGAPARTMENTS_MEDI      0.000000e+00  1.000000e+00\n",
       "NONLIVINGAREA_MEDI            0.000000e+00  1.000000e+00\n",
       "TOTALAREA_MODE                0.000000e+00  1.000000e+00\n",
       "OBS_30_CNT_SOCIAL_CIRCLE      0.000000e+00  3.480000e+02\n",
       "DEF_30_CNT_SOCIAL_CIRCLE      0.000000e+00  3.400000e+01\n",
       "OBS_60_CNT_SOCIAL_CIRCLE      0.000000e+00  3.440000e+02\n",
       "DEF_60_CNT_SOCIAL_CIRCLE      0.000000e+00  2.400000e+01\n",
       "DAYS_LAST_PHONE_CHANGE       -4.292000e+03  0.000000e+00\n",
       "AMT_REQ_CREDIT_BUREAU_HOUR    0.000000e+00  4.000000e+00\n",
       "AMT_REQ_CREDIT_BUREAU_DAY     0.000000e+00  9.000000e+00\n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    0.000000e+00  8.000000e+00\n",
       "AMT_REQ_CREDIT_BUREAU_MON     0.000000e+00  2.700000e+01\n",
       "AMT_REQ_CREDIT_BUREAU_QRT     0.000000e+00  2.610000e+02\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR    0.000000e+00  2.500000e+01\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_describe[col_missing].loc[['min','max'],:].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "application.fillna(-9999, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, precision_recall_curve, auc, average_precision_score, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV, cross_val_score\n",
    "\n",
    "import seaborn as sns\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = application.drop('TARGET', axis=1) \n",
    "Y = application['TARGET']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=123, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(246008, 121)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08072908198107379"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08072776937710356"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ind_params = { \n",
    "    'random_state':0, \n",
    "    'n_jobs': -1\n",
    "}\n",
    "\n",
    "cv_params = {\n",
    "    'penalty': ['l1'],\n",
    "    'class_weight':[{0:0.1,1:0.9}],\n",
    "    'max_iter': [300]\n",
    "}\n",
    "\n",
    "kfold = StratifiedKFold(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_lr = GridSearchCV(LogisticRegression(**ind_params),cv_params,cv=kfold,scoring='f1')\n",
    "clf_lr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(clf_lr.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_estimator_lr = clf_lr.best_estimator_\n",
    "best_estimator_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ind_params = { \n",
    "    'n_jobs':-1,\n",
    "    'random_state':0\n",
    "}\n",
    "\n",
    "cv_params = {\n",
    "    'n_estimators': [200],\n",
    "    'class_weight':[{0:0.1,1:0.9}, {0:0.2,1:0.8}],\n",
    "    'max_depth':[5,7]\n",
    "}\n",
    "\n",
    "kfold = StratifiedKFold(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "       error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_estimators': [200], 'class_weight': [{0: 0.1, 1: 0.9}, {0: 0.2, 1: 0.8}], 'max_depth': [5, 7]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='f1', verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_rf = GridSearchCV(RandomForestClassifier(**ind_params),cv_params,cv=kfold,scoring='f1')\n",
    "clf_rf.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_class_weight</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.776014</td>\n",
       "      <td>0.262999</td>\n",
       "      <td>0.354580</td>\n",
       "      <td>0.055211</td>\n",
       "      <td>{0: 0.1, 1: 0.9}</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'class_weight': {0: 0.1, 1: 0.9}, 'max_depth'...</td>\n",
       "      <td>0.266054</td>\n",
       "      <td>0.257435</td>\n",
       "      <td>0.270577</td>\n",
       "      <td>0.261676</td>\n",
       "      <td>0.261173</td>\n",
       "      <td>0.263383</td>\n",
       "      <td>0.004518</td>\n",
       "      <td>2</td>\n",
       "      <td>0.269384</td>\n",
       "      <td>0.270189</td>\n",
       "      <td>0.270012</td>\n",
       "      <td>0.272810</td>\n",
       "      <td>0.271832</td>\n",
       "      <td>0.270845</td>\n",
       "      <td>0.001272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.519579</td>\n",
       "      <td>0.628955</td>\n",
       "      <td>0.388340</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>{0: 0.1, 1: 0.9}</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>{'class_weight': {0: 0.1, 1: 0.9}, 'max_depth'...</td>\n",
       "      <td>0.270250</td>\n",
       "      <td>0.265811</td>\n",
       "      <td>0.273342</td>\n",
       "      <td>0.267315</td>\n",
       "      <td>0.267169</td>\n",
       "      <td>0.268777</td>\n",
       "      <td>0.002703</td>\n",
       "      <td>1</td>\n",
       "      <td>0.285929</td>\n",
       "      <td>0.284998</td>\n",
       "      <td>0.285462</td>\n",
       "      <td>0.286163</td>\n",
       "      <td>0.286705</td>\n",
       "      <td>0.285851</td>\n",
       "      <td>0.000585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.486022</td>\n",
       "      <td>0.789899</td>\n",
       "      <td>0.333187</td>\n",
       "      <td>0.058166</td>\n",
       "      <td>{0: 0.2, 1: 0.8}</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'class_weight': {0: 0.2, 1: 0.8}, 'max_depth'...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.486135</td>\n",
       "      <td>0.401199</td>\n",
       "      <td>0.415546</td>\n",
       "      <td>0.045578</td>\n",
       "      <td>{0: 0.2, 1: 0.8}</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>{'class_weight': {0: 0.2, 1: 0.8}, 'max_depth'...</td>\n",
       "      <td>0.023961</td>\n",
       "      <td>0.020568</td>\n",
       "      <td>0.019724</td>\n",
       "      <td>0.025509</td>\n",
       "      <td>0.016778</td>\n",
       "      <td>0.021308</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>3</td>\n",
       "      <td>0.025223</td>\n",
       "      <td>0.027761</td>\n",
       "      <td>0.021402</td>\n",
       "      <td>0.025467</td>\n",
       "      <td>0.022752</td>\n",
       "      <td>0.024521</td>\n",
       "      <td>0.002224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0      12.776014      0.262999         0.354580        0.055211   \n",
       "1      16.519579      0.628955         0.388340        0.003146   \n",
       "2      12.486022      0.789899         0.333187        0.058166   \n",
       "3      16.486135      0.401199         0.415546        0.045578   \n",
       "\n",
       "  param_class_weight param_max_depth param_n_estimators  \\\n",
       "0   {0: 0.1, 1: 0.9}               5                200   \n",
       "1   {0: 0.1, 1: 0.9}               7                200   \n",
       "2   {0: 0.2, 1: 0.8}               5                200   \n",
       "3   {0: 0.2, 1: 0.8}               7                200   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0  {'class_weight': {0: 0.1, 1: 0.9}, 'max_depth'...           0.266054   \n",
       "1  {'class_weight': {0: 0.1, 1: 0.9}, 'max_depth'...           0.270250   \n",
       "2  {'class_weight': {0: 0.2, 1: 0.8}, 'max_depth'...           0.000000   \n",
       "3  {'class_weight': {0: 0.2, 1: 0.8}, 'max_depth'...           0.023961   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.257435           0.270577           0.261676           0.261173   \n",
       "1           0.265811           0.273342           0.267315           0.267169   \n",
       "2           0.000000           0.000000           0.000000           0.000000   \n",
       "3           0.020568           0.019724           0.025509           0.016778   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  split0_train_score  \\\n",
       "0         0.263383        0.004518                2            0.269384   \n",
       "1         0.268777        0.002703                1            0.285929   \n",
       "2         0.000000        0.000000                4            0.000000   \n",
       "3         0.021308        0.003107                3            0.025223   \n",
       "\n",
       "   split1_train_score  split2_train_score  split3_train_score  \\\n",
       "0            0.270189            0.270012            0.272810   \n",
       "1            0.284998            0.285462            0.286163   \n",
       "2            0.000000            0.000000            0.000000   \n",
       "3            0.027761            0.021402            0.025467   \n",
       "\n",
       "   split4_train_score  mean_train_score  std_train_score  \n",
       "0            0.271832          0.270845         0.001272  \n",
       "1            0.286705          0.285851         0.000585  \n",
       "2            0.000000          0.000000         0.000000  \n",
       "3            0.022752          0.024521         0.002224  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(clf_rf.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight={0: 0.1, 1: 0.9},\n",
       "            criterion='gini', max_depth=7, max_features='auto',\n",
       "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=200, n_jobs=-1, oob_score=False, random_state=0,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_estimator_rf = clf_rf.best_estimator_\n",
    "best_estimator_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_params = { \n",
    "    'seed':0, \n",
    "    'scale_pos_weight': 1,\n",
    "    'min_child_weight': 1,\n",
    "    'objective': 'binary:logistic',\n",
    "    'base_score': Y_train.mean()\n",
    "}\n",
    "\n",
    "cv_params = {\n",
    "    'n_estimators': [200],\n",
    "    'max_depth':[5],\n",
    "    'max_delta_step':[0,2],\n",
    "    'learning_rate': [0.1,0.2,0.3] \n",
    "}\n",
    "\n",
    "kfold = StratifiedKFold(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "       error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.08072908198107379, colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       nthread=-1, objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_estimators': [200], 'max_depth': [5], 'max_delta_step': [0, 2], 'learning_rate': [0.1, 0.2, 0.3]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='f1', verbose=0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_xgb = GridSearchCV(xgb.XGBClassifier(**ind_params),cv_params,cv=kfold,scoring='f1')\n",
    "clf_xgb.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_max_delta_step</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.744682</td>\n",
       "      <td>0.840702</td>\n",
       "      <td>0.345921</td>\n",
       "      <td>0.021498</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_delta_step': 0, 'm...</td>\n",
       "      <td>0.040242</td>\n",
       "      <td>0.044552</td>\n",
       "      <td>0.043679</td>\n",
       "      <td>0.037763</td>\n",
       "      <td>0.034994</td>\n",
       "      <td>0.040246</td>\n",
       "      <td>0.003580</td>\n",
       "      <td>5</td>\n",
       "      <td>0.059509</td>\n",
       "      <td>0.061481</td>\n",
       "      <td>0.055492</td>\n",
       "      <td>0.066414</td>\n",
       "      <td>0.063855</td>\n",
       "      <td>0.061350</td>\n",
       "      <td>0.003732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.612994</td>\n",
       "      <td>0.582858</td>\n",
       "      <td>0.355462</td>\n",
       "      <td>0.024113</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_delta_step': 2, 'm...</td>\n",
       "      <td>0.044046</td>\n",
       "      <td>0.039349</td>\n",
       "      <td>0.035618</td>\n",
       "      <td>0.037333</td>\n",
       "      <td>0.032588</td>\n",
       "      <td>0.037787</td>\n",
       "      <td>0.003836</td>\n",
       "      <td>6</td>\n",
       "      <td>0.057554</td>\n",
       "      <td>0.058945</td>\n",
       "      <td>0.053897</td>\n",
       "      <td>0.061917</td>\n",
       "      <td>0.061490</td>\n",
       "      <td>0.058760</td>\n",
       "      <td>0.002918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.449763</td>\n",
       "      <td>0.229751</td>\n",
       "      <td>0.346128</td>\n",
       "      <td>0.012978</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.2, 'max_delta_step': 0, 'm...</td>\n",
       "      <td>0.057075</td>\n",
       "      <td>0.060764</td>\n",
       "      <td>0.053964</td>\n",
       "      <td>0.054515</td>\n",
       "      <td>0.053410</td>\n",
       "      <td>0.055946</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>3</td>\n",
       "      <td>0.108200</td>\n",
       "      <td>0.106706</td>\n",
       "      <td>0.103164</td>\n",
       "      <td>0.111540</td>\n",
       "      <td>0.107709</td>\n",
       "      <td>0.107464</td>\n",
       "      <td>0.002694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.671356</td>\n",
       "      <td>0.307696</td>\n",
       "      <td>0.341280</td>\n",
       "      <td>0.010225</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.2, 'max_delta_step': 2, 'm...</td>\n",
       "      <td>0.057646</td>\n",
       "      <td>0.054814</td>\n",
       "      <td>0.048968</td>\n",
       "      <td>0.045845</td>\n",
       "      <td>0.050669</td>\n",
       "      <td>0.051589</td>\n",
       "      <td>0.004191</td>\n",
       "      <td>4</td>\n",
       "      <td>0.098661</td>\n",
       "      <td>0.103241</td>\n",
       "      <td>0.098446</td>\n",
       "      <td>0.104861</td>\n",
       "      <td>0.101999</td>\n",
       "      <td>0.101441</td>\n",
       "      <td>0.002528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.497964</td>\n",
       "      <td>0.125883</td>\n",
       "      <td>0.334799</td>\n",
       "      <td>0.009407</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.3, 'max_delta_step': 0, 'm...</td>\n",
       "      <td>0.071228</td>\n",
       "      <td>0.068177</td>\n",
       "      <td>0.063252</td>\n",
       "      <td>0.066108</td>\n",
       "      <td>0.065222</td>\n",
       "      <td>0.066797</td>\n",
       "      <td>0.002722</td>\n",
       "      <td>1</td>\n",
       "      <td>0.159327</td>\n",
       "      <td>0.162393</td>\n",
       "      <td>0.152852</td>\n",
       "      <td>0.160128</td>\n",
       "      <td>0.157478</td>\n",
       "      <td>0.158435</td>\n",
       "      <td>0.003207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51.149282</td>\n",
       "      <td>0.620890</td>\n",
       "      <td>0.338327</td>\n",
       "      <td>0.019575</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.3, 'max_delta_step': 2, 'm...</td>\n",
       "      <td>0.064244</td>\n",
       "      <td>0.066151</td>\n",
       "      <td>0.064136</td>\n",
       "      <td>0.051728</td>\n",
       "      <td>0.056026</td>\n",
       "      <td>0.060457</td>\n",
       "      <td>0.005588</td>\n",
       "      <td>2</td>\n",
       "      <td>0.154936</td>\n",
       "      <td>0.159420</td>\n",
       "      <td>0.148513</td>\n",
       "      <td>0.152309</td>\n",
       "      <td>0.162267</td>\n",
       "      <td>0.155489</td>\n",
       "      <td>0.004909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0      51.744682      0.840702         0.345921        0.021498   \n",
       "1      51.612994      0.582858         0.355462        0.024113   \n",
       "2      49.449763      0.229751         0.346128        0.012978   \n",
       "3      50.671356      0.307696         0.341280        0.010225   \n",
       "4      49.497964      0.125883         0.334799        0.009407   \n",
       "5      51.149282      0.620890         0.338327        0.019575   \n",
       "\n",
       "  param_learning_rate param_max_delta_step param_max_depth param_n_estimators  \\\n",
       "0                 0.1                    0               5                200   \n",
       "1                 0.1                    2               5                200   \n",
       "2                 0.2                    0               5                200   \n",
       "3                 0.2                    2               5                200   \n",
       "4                 0.3                    0               5                200   \n",
       "5                 0.3                    2               5                200   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0  {'learning_rate': 0.1, 'max_delta_step': 0, 'm...           0.040242   \n",
       "1  {'learning_rate': 0.1, 'max_delta_step': 2, 'm...           0.044046   \n",
       "2  {'learning_rate': 0.2, 'max_delta_step': 0, 'm...           0.057075   \n",
       "3  {'learning_rate': 0.2, 'max_delta_step': 2, 'm...           0.057646   \n",
       "4  {'learning_rate': 0.3, 'max_delta_step': 0, 'm...           0.071228   \n",
       "5  {'learning_rate': 0.3, 'max_delta_step': 2, 'm...           0.064244   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.044552           0.043679           0.037763           0.034994   \n",
       "1           0.039349           0.035618           0.037333           0.032588   \n",
       "2           0.060764           0.053964           0.054515           0.053410   \n",
       "3           0.054814           0.048968           0.045845           0.050669   \n",
       "4           0.068177           0.063252           0.066108           0.065222   \n",
       "5           0.066151           0.064136           0.051728           0.056026   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  split0_train_score  \\\n",
       "0         0.040246        0.003580                5            0.059509   \n",
       "1         0.037787        0.003836                6            0.057554   \n",
       "2         0.055946        0.002717                3            0.108200   \n",
       "3         0.051589        0.004191                4            0.098661   \n",
       "4         0.066797        0.002722                1            0.159327   \n",
       "5         0.060457        0.005588                2            0.154936   \n",
       "\n",
       "   split1_train_score  split2_train_score  split3_train_score  \\\n",
       "0            0.061481            0.055492            0.066414   \n",
       "1            0.058945            0.053897            0.061917   \n",
       "2            0.106706            0.103164            0.111540   \n",
       "3            0.103241            0.098446            0.104861   \n",
       "4            0.162393            0.152852            0.160128   \n",
       "5            0.159420            0.148513            0.152309   \n",
       "\n",
       "   split4_train_score  mean_train_score  std_train_score  \n",
       "0            0.063855          0.061350         0.003732  \n",
       "1            0.061490          0.058760         0.002918  \n",
       "2            0.107709          0.107464         0.002694  \n",
       "3            0.101999          0.101441         0.002528  \n",
       "4            0.157478          0.158435         0.003207  \n",
       "5            0.162267          0.155489         0.004909  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(clf_xgb.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.08072908198107379, colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.3, max_delta_step=0,\n",
       "       max_depth=5, min_child_weight=1, missing=None, n_estimators=200,\n",
       "       nthread=-1, objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_estimator_xgb = clf_xgb.best_estimator_\n",
    "best_estimator_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c9865f8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H0bt3P95/vzkqlQovLx+WLfNh6NBBHDy4nyVLVqBSqUr7UvKROwZvqKenEsXHx5OWlsbK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o6JTrvoWTiNu1a8enn35KamoqhoaGJCYmcv36dVq2bFnmJ/9FZWZmsn37diZOnKi2TVhYGG5u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epxAwcOZNGiRYwaNYqDBw9ibW3N8ePHgfzU5mHDhintr6enR3p6eol5CDVr1uTp06cArFq1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Chw4d+Oqrr/Dx8eLo0aOqPZV4lkkipTqkjuqQOqpD6qgOqaM6pI66/eUDg8jISIYPH07z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c9864a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,20))\n",
    "xgb.plot_importance(best_estimator_xgb, ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Final Model & Model Evaluation\n",
    "### 3.1 Training and scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# final_lr = best_estimator_lr.fit(X_train, Y_train)\n",
    "final_rf = best_estimator_rf.fit(X_train, Y_train)\n",
    "final_xgb = best_estimator_xgb.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "# pred_lr = final_lr.predict(X_test)\n",
    "pred_rf = final_rf.predict(X_test)\n",
    "pred_xgb = final_xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print('Confusion Matrix (LR):')\n",
    "\n",
    "# df_cm = pd.DataFrame(confusion_matrix(Y_test, pred_lr))\n",
    "# fig, ax = plt.subplots(figsize=(10,8))\n",
    "# sns.heatmap(df_cm, annot=True, fmt=\"d\", ax=ax)\n",
    "# ax.set_ylabel('True label');\n",
    "# ax.set_xlabel('Predicted label');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (RF):\n"
     ]
    },
    {
     "data": {
      "image/png": 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slStTRinJI1W2TJlAXRoCzHrbi+IHl/XtgQOaMu0lLUz9L5UtU0bpmzar/+Dh\nmjphrMZNekFzX3tFlSpW0IQpL2rarFeUNPRvysvL0/yF7+q/3pqnC1lZF53vs893aVD/p9TuL3cG\n6IqAq8O3+//3vTf39X+99/42TD0e6KLTp8/o/bS5crvdeuix3lq5eo3atrlTO3Z+rrZtWmvU8CEX\nnSs3N1fPDB6uSeNGq1GD+lrw7ntKSk7RrBcnB+jqgMBizQ8uK7RYqJ4bPsT7G2KdWrV04uRJvb9s\nue69u70qVawgSerz2F/18IPdJUl7vvhSX+37RlMnpvzifDt2fq7lK/+hTvEP6vGnBujLr/f572KA\nq0hoaKieS/z39971OnHypOK7dNaklGQFBQXpzNlzOn8+U8VjYyVJn+38XN/uP6AuPXrqgYf+qtUf\nrZMk7frvPYqOilKjBvUlSZ06dtDWbZ/qzJmzAbk2INBIfnBZlSpW8BY4Ho9Hk6ZOU6sWzXToyBFF\nRUXpqWcH6/Dho7quejX9bUA/SVK9OrVVr05tHTp85KJzXcjK0rVVqqhXQg81iWuoD1etUe/+A7Vs\n4XxFRkb6/dqAK9kv3nsv/PTeK1asmCTphekz9fY7i1Sn1vWKa9RQkhQRHq677rxD93e6Rwe++14P\nP95HFa4pr6PHjuma8uW85y5WrJhKliyhY8ePX9SqBqwg+cGvciErSwOHjtD3Bw9q1PAhysvL0/oN\nGzVyyN/0ztzXVaZ0aY0aO+Gy54iMiNAr019Qk7if/qL+S+vbFRsTo13/vccflwBclX567yXq+4OH\nNCpxqPf4gKf6aNNHK1WxQgWNGT9JkpQ4ZJC63X+fgoODVe3aqmrT+g6t27BJbrfnl7vaeTwKDuKf\nALMcHz2uEj5JfhISEpSbm3vRMY/HI8dxlJaW5ouXhA8dOXpUfZ8ZrGrXVtFrM2coPDxM5cqUUY3q\n1VWmTGlJ0j3t2+qvT/a77HkOHzmqtekb1L3r/f866PEoJIQAEriUn957f1O1qlX02qyf3nv//Gyn\nSpYooapVKqtYSIju6dBW4yZNUX5+vl57M1Xdu96vqKgoST/9vRsSEqwK15TX8RMnvOfNzcvTmbPn\nVK5c2UBdGhBQPvlX59lnn1ViYqJeeuklBQcH++Il4Ccul0sPP/GUOra7S70f7eU93vr2Vpr84gw9\n2vNBlShRXKvXrVfdWrUue66IiHDNeHmO6teto3p1ait902Zl/ZitenVq+/oygKuOy+XSw4/3Vcf2\nd6n3o3/1Ht+6LUM7d+3StOcnKCgoSMs/WKmbmjRWcHCw1qVvVFhoqB7qEa/DR45o9Ufr9Nqs6frj\nH/+gM2fPasdnn6thg3p6f+nl16cTAAAMAklEQVTf1aBeXcXGxATwChFI3O3lAw0aNFDHjh31xRdf\nqHXr1r54CfjJ2+8s0pGjR7Vm3XqtWbfee/zVl6apR7eueviJJ+X2eFThmms0OnHIZc4klSxRQs+P\nS9bolInKzc1VVFSUXpw4zruGAcC/vL3wf997a9O1Zm269/jL01/QiRMn1Dn+ITlBjuIaNtDTfXtL\nksYnj9LolIla/PcVcrvdGvzM06p2bVVJ0tSJKRo3cbKysn5U8RKxGjcqKQBXhSuFY3yfH8fj8XgC\nPYn/K+fsicIHASh6xn8bBAIpNLa0317r+7+v8Ml5/9i+rU/OW9RYbAEAgDXGf9FhqT8AADCF5AcA\nAGOsL3gm+QEAAKZQ/AAAAFNoewEAYI3trhfJDwAAsIXkBwAAY6xvckjxAwCANdztBQAAYAfJDwAA\nxrDPDwAAgCEUPwAAwBSKHwAAYAprfgAAsIZb3QEAgCUseAYAADCE5AcAAGtsBz8kPwAAwBaSHwAA\njGHNDwAAgCEUPwAAwBTaXgAAWGN8nx+SHwAAYArJDwAAxlhf8EzxAwCANcaLH9peAADAFJIfAACM\nsd72IvkBAACmUPwAAABTKH4AAIAprPkBAMAa45scUvwAAGAMC54BAAAMIfkBAMAakh8AAAA7SH4A\nADDGMb7gmeQHAACYQvEDAABMoe0FAIA1LHgGAACwg+QHAABjrG9ySPEDAIA1xosf2l4AAMAUkh8A\nAIxhnx8AAABDSH4AAIBf5ObmasiQITp06JCCgoKUnJyskJAQDRkyRI7j6LrrrtPIkSMVFBSkGTNm\naN26dQoJCdGwYcNUv359HThw4JJjfyuSHwAA4Bfr169XXl6e0tLS9OSTT2rq1KlKSUlR//79NX/+\nfHk8Hq1Zs0a7d+/WJ598onfeeUdTpkzRc889J0mXHPt7UPwAAGCN4/jmUYhrr71W+fn5crvdyszM\nVEhIiHbv3q0bb7xRktSiRQtt3rxZGRkZatasmRzHUcWKFZWfn69Tp05dcuzvQdsLAABrAnSre2Rk\npA4dOqS77rpLp0+f1ssvv6xt27Z59x2KiorS+fPnlZmZqRIlSnif9/Nxj8fzi7G/B8UPAADwizfe\neEPNmjXTwIEDdeTIET300EPKzc31ft/lcik2NlbR0dFyuVwXHY+Jiblofc/PY38P2l4AABjjOI5P\nHoWJjY1VTEyMJKl48eLKy8tT7dq1tXXrVklSenq6mjRpori4OG3cuFFut1uHDx+W2+1WqVKlLjn2\nd12/x+Px/K5n+lDO2ROBngJgk/FdX4FACo0t7bfXOv15hk/OW7Je48t+3+VyadiwYTp+/Lhyc3P1\n4IMPqm7duhoxYoRyc3NVrVo1jRkzRsHBwZo+fbrS09Pldrs1dOhQNWnSRN9+++0lx/5WFD8A/oXi\nBwgYvxY/u7f75Lwl68T55LxFjbYXAAAwheIHAACYwt1eAAAY4zi2sw/bVw8AAMwh+QEAwBrjNzdQ\n/AAAYMyv2ZPnPxltLwAAYArJDwAA1gSR/AAAAJhB8QMAAEyh+AEAAKaw5gcAAGOs3+1F8QMAgDXG\nix/aXgAAwBSSHwAArOGzvQAAAOwg+QEAwBiHTQ4BAADsoPgBAACm0PYCAMAabnUHAACwg+QHAABj\n2OEZAADYwj4/AAAAdpD8AABgDPv8AAAAGELxAwAATKH4AQAAprDmBwAAa7jVHQAAWGJ9nx/aXgAA\nwBSSHwAArGGTQwAAADtIfgAAsIZNDgEAAOyg+AEAAKbQ9gIAwBhudQcAADCE5AcAAGuM3+pO8QMA\ngDG0vQAAAAwh+QEAwBrjbS/bVw8AAMyh+AEAAKZQ/AAAAFNY8wMAgDGO8c/2ovgBAMAabnUHAACw\ng+QHAABjHG51BwAAsIPkBwAAa4yv+XE8Ho8n0JMAAADwF9peAADAFIofAABgCsUPAAAwheIHAACY\nQvEDAABMofgBAACmUPygSLjdbiUlJalr165KSEjQgQMHAj0lwJTPPvtMCQkJgZ4GcFVgk0MUidWr\nVysnJ0cLFizQjh07NH78eM2aNSvQ0wJMmDNnjpYuXaqIiIhATwW4KpD8oEhkZGSoefPmkqSGDRtq\n165dAZ4RYEflypU1ffr0QE8DuGpQ/KBIZGZmKjo62vt1cHCw8vLyAjgjwI42bdooJIQgH/i1KH5Q\nJKKjo+Vyubxfu91u/jIGAFyRKH5QJOLi4pSeni5J2rFjh2rUqBHgGQEAcGn8ao4i0bp1a23atEnd\nunWTx+PRuHHjAj0lAAAuiU91BwAAptD2AgAAplD8AAAAUyh+AACAKRQ/AADAFIofAABgCsUPEEAH\nDx5U3bp11bFjR91zzz1q166dHn74YR09evR3n/O9997TkCFDJEmPPvqojh07VuDYadOm6dNPP/1N\n569Zs+Yvjk2fPr3Qj1e47bbbdPDgwV/9Or/mnADwe1D8AAFWrlw5LVmyRIsXL9by5ctVs2ZNTZw4\nsUjOPWfOHJUvX77A72/btk35+flF8loAcLVgk0PgCnPTTTdpypQpkn5KS+rXr689e/Zo/vz52rBh\ng95880253W7VqVNHI0eOVFhYmBYvXqxZs2YpOjpalSpVUmRkpPf5b731lsqWLavnnntOGRkZKlas\nmPr06aOcnBzt2rVLiYmJmjFjhsLDwzVq1CidOXNG4eHhGjFihGrXrq2DBw9q0KBBunDhgho0aFDo\n/OfOnaslS5YoKytLxYoV0+TJk1WtWjVJ0owZM7R3716FhYXpueee0/XXX68TJ04oKSlJR48eleM4\nGjhwoG699Vbf/YABmEfyA1xBcnNztXLlSjVs2NB7rEWLFlq5cqVOnTqlhQsXKi0tTUuWLFHp0qX1\n2muv6dixY3r++ec1b948LViw4KLPWPtZamqqLly4oA8++ECvv/66XnrpJbVt21Z169bVmDFjVLNm\nTQ0ePFiDBg3S+++/r+TkZA0YMECSlJycrE6dOmnJkiWKi4u77PwzMzO1evVqpaam6u9//7v+/Oc/\na968ed7vV6lSRYsXL1afPn28rbmxY8fqvvvu03vvvadZs2YpKSlJmZmZRfHjBIBLIvkBAuyHH35Q\nx44dJUk5OTmqX7++Bg4c6P3+z2nL1q1bdeDAAXXp0kXST4VS7dq19c9//lONGjVSmTJlJEkdOnTQ\nxx9/fNFrbNu2TV26dFFQUJDKli2r5cuXX/R9l8ulXbt2aejQod5jFy5c0OnTp/XJJ59o8uTJkqS7\n775biYmJBV5LdHS0Jk+erOXLl2v//v3asGGDatWq5f3+/fffL0lq2bKlBg0apHPnzmnz5s365ptv\nNG3aNElSXl6evv/++9/wEwSA34biBwiwn9f8FCQsLEySlJ+fr7vuustbfLhcLuXn52vLli3690+p\nCQn55ds6JCREjuN4vz5w4IAqVKjg/drtdis0NPSieRw9elQlSpSQJO/5HcdRUFDBgfGRI0eUkJCg\nHj16qEWLFipTpoz27Nnj/X5wcLD3zx6PRyEhIXK73XrzzTe9r/XDDz+odOnSWr16dYGvAwD/H7S9\ngKvETTfdpFWrVunkyZPyeDwaNWqU3nzzTTVu3Fg7duzQsWPH5Ha7tWLFil8894YbbtCKFSvk8Xh0\n8uRJ9ejRQzk5OQoODlZ+fr5iYmJUtWpVb/GzadMmde/eXZJ06623aunSpZKkf/zjH8rOzi5wjp9/\n/rmqVKminj17ql69elq9evVFC6qXLVsmSVq1apX+9Kc/KTIyUjfffLPmz58vSfr666/VoUMHZWVl\nFc0PDQAugeQHuEpcf/316tu3rx566CG53W7VqlVLjz32mMLCwpSYmKiePXsqIiJC1atX/8Vz4+Pj\nNWbMGN19992SpBEjRig6OlrNmzfXyJEjNWHCBE2aNEmjRo3Sq6++qmLFiumFF16Q4zhKSkrSoEGD\ntGDBAtWtW1dRUVEFzrFp06Z6++231bZtW3k8Ht1www366quvvN/fv3+/OnbsqKioKI0fP16SlJiY\nqKSkJHXo0EGSNHHiREVHRxfljw4ALsKnugMAAFNoewEAAFMofgAAgCkUPwAAwBSKHwAAYArFDwAA\nMIXiBwAAmELxAwAATKH4AQAApvwP5uhDINAdczEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd0d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix (RF):')\n",
    "\n",
    "df_cm = pd.DataFrame(confusion_matrix(Y_test, pred_rf))\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.heatmap(df_cm, annot=True, fmt=\"d\", ax=ax)\n",
    "ax.set_ylabel('True label');\n",
    "ax.set_xlabel('Predicted label');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (XGB):\n"
     ]
    },
    {
     "data": {
      "image/png": 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AwBaaHwAAjLE+7UXzAwAATKH5AQDAGI/t4ofmBwAA2EL4AQAApjDtBQCAMSx4BgAAMITm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\nCD8AAMAUwg8AADDl/wFUVGh9udG35gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10edf9470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix (XGB):')\n",
    "\n",
    "df_cm = pd.DataFrame(confusion_matrix(Y_test, pred_xgb))\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.heatmap(df_cm, annot=True, fmt=\"d\", ax=ax)\n",
    "ax.set_ylabel('True label');\n",
    "ax.set_xlabel('Predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 ROC & PR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Logistic Regression\n",
    "# prob_lr = pd.DataFrame(list(final_lr.predict_proba(X_test)))\n",
    "# fpr_lr, tpr_lr, thresholds_lr = roc_curve(y_test, prob_lr.iloc[:,1])\n",
    "# roc_auc_lr = auc(fpr_lr, tpr_lr)\n",
    "# p_lr,r_lr,thre_lr = precision_recall_curve(y_test, prob_lr.iloc[:,1])\n",
    "# average_p_lr = average_precision_score(y_test, prob_lr.iloc[:,1])\n",
    "\n",
    "# Random Forest\n",
    "prob_rf = pd.DataFrame(list(final_rf.predict_proba(X_test)))\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(Y_test, prob_rf.iloc[:,1])\n",
    "roc_auc_rf = auc(fpr_rf, tpr_rf)\n",
    "p_rf,r_rf,thre_rf = precision_recall_curve(Y_test,prob_rf.iloc[:,1])\n",
    "average_p_rf = average_precision_score(Y_test, prob_rf.iloc[:,1])\n",
    "\n",
    "# xgboost\n",
    "prob_xgb = pd.DataFrame(list(final_xgb.predict_proba(X_test)))\n",
    "fpr_xgb, tpr_xgb, thresholds_xgb = roc_curve(Y_test, prob_xgb.iloc[:,1])\n",
    "roc_auc_xgb = auc(fpr_xgb, tpr_xgb)\n",
    "p_xgb,r_xgb,thre_xgb = precision_recall_curve(Y_test,prob_xgb.iloc[:,1])\n",
    "average_p_xgb = average_precision_score(Y_test, prob_xgb.iloc[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8zmdjikrMPPPRDtKyi+jTMYq7J3ehYYivA6MVEWdR/ygiIjXBzTAMw9lBnI/q\nmtqjIWF7ahd7F9ImReZi3k1ewr6sA9za5cayqZeVycgp5rWvkvD28uCeqV3w9Kh6MZXaQN8n9tQm\n9jQV0zkutc0XrPqAiNAgbu45rpoiqhv0M25PbWJPbVIxtYs9l52KKVIfbEvbwdL9X9CuQRvm959L\nkHdgpWWLSy289/0+ftmTRte24dw8tqPLJHUiIiIiUrspsRO5CEXmIt7f+ym7MpKY1G4sg5tfVmnZ\n42n5rPjlGFuS0+ncOownbu5D44YBDoxWREREROo6JXYiF6jQXMQ/f3matiGtmN9/DmG+oRWWyy00\n8e0vx1i59QSDuzVlwS19aRTm7+BoRURERKQ+UGIncgG2nk7g433L6BLRies7Tqu03LHT+fzz7a1E\nRwVy77SudGoV5sAoRURERKS+UWInUoUicxE/pmxkzYmfKLGUclWrEYxqNbTS8vlFJp77dCcje0cz\ndci5F1IREREREakOSuxEzuF4Xgov7XyDYO8grm47ll5RXfHy8KqwrGEYbE5O45O1h+jcKowpg9s4\nOFoRERERqa+U2IlUYl/WQV7c+TpXtRrOlS2GVLovncVq41haPp+sPcT+EzncPKYj/To3cnC0IiIi\nIlKfKbET+R8llhJe2/oV649uZlzrkQxvcUWlZX/Zc5p3vtuHp7sbvTtGMXtKPL7e+rESEREREcfS\nHajIHxRbSnhyy3P4eftwf887aRJY8cjbwZRclv10mORj2Vw/MoYrujZ1cKQiIiIiIr9TYicC5JsK\nWHtiA98fW0OTgEY8PeJBsrOK7codT8vnyfd/pdRsZeKg1tw1KR4fbw8nRCwiUn1OuydzMs8EjHN2\nKCIicpGU2Em9ZrVZ+eTAV2xK3UJMWDtu6HgNPaO64ulR/kdj2950tu1LZ8eBTPp0jOJPI9rj5amE\nTkTqBqubydkhiIjIJVJiJ/VW0pl9fH34O0w2C7d3nUn7UPtVLEtMFh5/dzupmYV0adOQ2VO6ENui\n4g3JRUREREScRYmd1Ds2w8aSfcvYkLqZy5sNYFzrkfh6+pQrYxgGX204wpcbjxDXuiEPzeiBn49+\nXERERESkdtKdqtQrB3OO8OHez3ADHu5zL40CouzKJB3JYulbW8jOK+XPI2MZ1KWJ4wMVEREREbkA\nSuyk3tiffYgXd7zOgCZ9uLrtVXYbjVttNj798RCrtqYwfUQMAztH4eOl5+hEREREpPZTYif1ws6M\nRF7b/S6dG3ZgWsyEcucMw2DV1hN8vOYgjcL8mTujO327NCMjI99J0YqIiIiIXBgldlKnpRac5rXd\n75Bnymda+wkMata/3HmbYfCCsRXLAAAgAElEQVSfpTs5npbPnZPi6NI2HHc3NydFKyIiIiJycZTY\nSZ2UXZLDkv3L2J2ZzKiWwxjR4gq8PbzLlUnLLuKlz3dTYrLy2Mw+BPt7V3I1EREREZHaTYmd1Ckl\nllLWnFjPt0d/oEt4JxZeNo8g70C7cuk5xTz9YQJNwgO4f2xHgpTUiYiIiIgLU2IndcaJ/FQWJbxG\niE8wt8X/hQ4N21dYLjOnmDmLNzGqbzSTL2+Dm6ZeioiIiIiLU2IndcKeM/t4aecbDIu+nAltRlea\nrO04kMniLxO5oltTplzR1sFRioiIiIjUDCV24vJWHFnFN0dW8ZdO19IjqmuFZTJyinlp2W6OpxUw\nc0wH+ndu7OAoRURERERqjhI7cVlHco+zdP8XpBSk8lDvv9EksFGF5XYezOT5T3fRIyaCe6Z2JSRA\nz9OJiIiISN2ixE5c0tbTCby95yNGthjCbV3+UuECKVabjRWbjvHFT0eYNb4TvTtEOSFSEREREZGa\np8ROXE5WSTbv7PmY6ztMo0/jHhWWSc8u4t3v95GRU8zMsR2V1ImIiIhInabETlzK7sw9vJX0IaNa\nDas0qVu97QQfrj5A59ZhPHpjb/x89G0uIiIiInWb7njFJZzdcPwL9pzZxw2drqF7ZLxdmYJiM68t\nTyLxcBb3T+9Kh5ZhTohURERERMTxlNhJrbcv6yCLd79NbGg75vefQwOfELsypWYrT76/nUZh/vzr\ntv6EBfs6IVIREREREedQYie1ltlm4f3kpWxL28FNnf9U4SgdwLHT+bz6VRKhQT7cfnUc7tpwXERE\nRETqGSV2UitllWSzYMtzRPqFM6/v/UT5R1RYbuvedF79MonB3ZoydUgbJXUiIiIiUi8psZNaZ8vp\nX/l43+f0jOrK9JircXdzr7DcodRcFn+ZyF2T4unSNtzBUYqIiIiI1B5K7KRW2XY6gXf3LOH6jtPo\n3ah7peW2JKex+Mskpg5uq6ROREREROo9JXZSa+SU5vLe3k+4JubqSpM6wzB49/t9rNuRqpUvRURE\nRET+nxI7qRWSz+znraQP6RYRx4CmfSouczSLz9YfJrfApJUvRURERET+QImdOF12SQ7/TXyX4dGD\nGdlySIVlDqTk8OzSnQzq2oSpg9vi4+Xh4ChFRERERGovJXbiVCcLTvF8wqv0iurGqFZDKyyz91g2\nz3+2i0mXt2Fkn2gHRygiIiIiUvspsROnyTcVsGjHf7m8aX9Gtxpud94wDL7ccISvNh7luuHtGdqj\nmROiFBERERGp/ZTYiVOYrCae2baIKP8Irmo9wu78wZRc/vPJDvx8PLl3Wlc6tdIiKSIiIiIilVFi\nJw639XQC7+z5mGDvIG7vcpPdeZvN4L9fJzGqTwvG9G/p+ABFRERERFyMEjtxmMziLL4+vJKtab/y\np9gp9GvSy66MYRi8+lUS/r5eSupERERERM6T+/kUKioqYu/evRiGQVFRUU3HJHVQQvpuHtm0EAMb\nj/S9v8KkDuCLn46wPyWHe6d1dXCEIiIiIiKuq8rEbtOmTYwfP57bbruNzMxMBg8ezIYNGxwRm9QR\nR3KP8V7yEu7sejM3drqWSP8IuzJWm43vNh/nm03HuG96NwL9vJwQqYiIiIiIa6oysXv22Wf58MMP\nCQ4OJiIigg8++ICnn37aEbFJHZCctZ9/bX+JiW3HEBvWrsIy+UUm5izexM+Jp7lrchxNwwMcHKWI\niIiIiGur8hk7m81GRMTvIyxt27at0YCk7kgrTOfVXe9wS9z1dInoXGm5t7/dS/vmDZg5piNubm4O\njFBEREREpG6ocsSuUaNGrF27Fjc3N/Ly8njllVdo0qSJI2ITF2axWXh7z8cMatrvnEndsdP57Dp0\nhqsHtVFSJyIiIiJykapM7ObPn8/y5cs5deoUw4cPJzk5mccee8wRsYmLyi7JYc6Gx7AaVsa3GVVp\nuWOn83nyg+2Mu6wVDUN8HRihiIiIiEjdUuVUzL179/Lss8+WO7Zy5UpGjLDfVFqk1Gri1d3v0DOq\nK9PaT6h0FO7QyVyeeG87g7s1ZUy/Fg6OUkRERESkbqk0sVuxYgUmk4kXXniBu+66q+y4xWLh1Vdf\nVWIndk7kp7Io4TWig5sxud3YSpO6TYmn+e/Xexg3oCUTBrZ2cJQiIiIiInVPpYldYWEhv/76K4WF\nhWzevLnsuIeHB/fcc49DghPXkW8q4PXd7zK4+UBGtRpaabmUjALeXJHM36Z2oXPrhg6MUERERESk\n7qo0sZsyZQpTpkxh06ZN9OvXz5ExiYspMBcyZ8N8OoS1Z2TLIZWWy84v5akPfmVU32gldSIiIiIi\n1ajKZ+z8/Py49dZbKSoqwjAMbDYbqamprFmzxhHxiQtYsm8Z8eGd+Gv8nystk5JRwLNLdhDbIpSJ\nmn4pIiIiIlKtqlwV88EHH2TYsGFYrVauu+46oqKiGDZsWJUXttlszJs3j2nTpjFjxgyOHTtW7vy6\ndeuYOnUqU6dO5dFHH8UwjIv/FOI0G09uZs+Z/UyPmVhpmf0ncljw3nb6dmrEbRM6a1sDEanX1D+K\niEhNqDKx8/b2ZtKkSfTu3Zvg4GCefvppNmzYUOWFV69ejclkYsmSJdx7770sXLiw7FxBQQHPPPMM\nixcvZunSpTRt2pTs7OxL+yTicNvSdvDRvs+5vetfCPEJrrBMenYRCz/4lT+NaM/UwW2V1IlIvaf+\nUUREakKViZ2Pjw85OTm0atWKnTt34uHhgdVqrfLC27dvZ+DAgQB07dqVxMTEsnMJCQm0b9+ep556\nimuvvZbw8HDCwsIu4WOIox3PS+GtpA+5tcuNtA5pWWGZY6fzmffGFsb0b0n/zo0dG6CISC2l/lFE\nRGpClc/Y3XDDDdxzzz0sWrSIKVOmsHz5cjp37lzlhQsKCggMDCx77eHhgcViwdPTk+zsbDZv3swX\nX3yBv78/1113HV27dqVVq1aVXi801B9PT4/z/FiVi4gIuuRr1EUX0i6lFhPvb13KiDaDuCK2V4Vl\nzBYrj769lSnD2jNtWHuXHKnT94o9tYk9tYk9tcm5VXf/COoja5LaxJ7axJ7apGJqF3s12SZVJnaj\nRo1i5MiRuLm58dlnn3H06FGio6OrvHBgYCCFhYVlr202G56eZ9+uQYMGxMXFERERAUDPnj1JTk4+\nZ8eVnV1U5XtWJSIiiIyM/Eu+Tl1zIe1iGAavJ76Hp5sXo5uPrLBeTkEpz3yUgK+3B4O7NCYzs6C6\nQ65x+l6xpzaxpzaxV11tUpdvBqq7f4Tq6SMBfT//D/2M21Ob2FObVEztYq862uRc/WOlUzGzsrL4\n97//zeuvv1429dLX15eEhITz2py8e/furF+/HoAdO3bQvn37snOdO3dm//79ZGVlYbFY2LlzJ23b\ntj3vDyTOYRgGnx1YztG8E9zRZSZe7vZ/F7DabLzyRSIhAd7M/VMP3F1wpE5EpCbV9v6xxFKKxWZx\n6HuKiMilq3TE7r777iMgIIDs7GzMZjPDhw/nb3/7G4WFhcydO7fKCw8fPpyNGzcyffp0DMNgwYIF\nvPXWW0RHRzN06FDuvfdeZs6cCcDIkSPLdWxS+9gMG+8nf0JiZjL39rwdfy+/Cst9teEoJouNOdd1\nx9Ojykc4RUTqndrcP1ptVv7+06N0jYzjxk7XOux9RUTk0rkZlayjPGzYMFavXk1BQQHTp08nNzeX\nGTNmcMMNN+Dt7e3oOKttao+GhO1V1S4FpkKeT3iVUmspd3ebRUO/0ArLJezP4OUvEnlqVj/Cgn1r\nKlyH0PeKPbWJPbWJPU3FdI5LbfPb1zxQ7nXTwMY82PueS7pmXaCfcXtqE3tqk4qpXezV9FTMSkfs\nfnuwOzAwkJycHBYtWkS3bt0uKRBxPSWWUv7z6ys0CojiutjJlY7UFZdaePf7fVw9qLXLJ3UiIiIi\nIq6m0sTuj6sYhoeHK6mrhw7nHuW95KU09A1jZuc/Vbqypcls5ZE3txAdFcSVfapeWEdERGq3AE9/\nZ4cgIiIXqNLErrCwkG3btmGz2SguLmbbtm38cdZmr14VL3MvdUOp1cRru98lPrwT09pPqDSpMwyD\nl79IJDLUj9lT4l1yWwMRESnvZMEpZ4cgIiIXqNLELioqiueffx6AyMjIsq/h7Gjeu+++W/PRiVNY\nbVZeT3yPCL+GXBNz9TlH6l7/JpkT6QU8PrOPkjoRkTqi0FI92yeIiIjjVJrYvffee46MQ2qRX05v\n42R+Kg/2/lulyVpBsZm7nv+JkABv5t3QCz+fKrdEFBERERGRGqK7cSlnb9YBPtr7OXd1u5lA74AK\ny+QUlPK3FzcS7O/Fglv6KqkTkXrt5MmTvP/+++Tm5pZ7ZOHJJ590YlQiIlLf6I5cyuSU5vLq7neY\nHjOR9qEVb4ibll3Ek+9tZ1jPZlw7THsPiojMnj2bnj170rNnT01JFxERp1FiJ2U+2vs57Ru04bKm\nfSs8X2Ky8PSHCQzs0oRJl7dxcHQiIrWTxWLh73//u7PDEBGRes69qgK5ubn84x//4PrrrycnJ4e5\nc+eSm5vriNjEgdYcX0/imWSmxUyo8LxhGLzyRRJhwT5cPai1g6MTEam9evTowZo1azCZTM4OpVr9\ncVqpiIjUflUmdg8//DBxcXHk5OTg7+9PZGQk999/vyNiEwcptZhYcXQ1t8bfSJhvqN15s8XKS8sS\nycwt5p4pXTXVSETkD7777jtuu+024uPjiY2NJTY2lg4dOjg7rEtWaq1biaqISF1X5VTMlJQUpk2b\nxkcffYS3tzf33HMP48aNc0Rs4iDv7fiMxgGN6BxufyOSlVfC3xdvIjoqiH9c31MLpYiI/I8NGzY4\nO4Qa4e5W5d9+RUSkFqnyLt3Dw4P8/PyyUZqjR4/i7q5f9nWB2Wrm6yMrWX9yE4/2nWN33jAMFn2+\nm0Fdm/Cn4e01UiciUoHi4mJefPFFNm3ahNVqpW/fvtx99934+/s7OzQREalHqszQ7rzzTmbMmEFq\naiq33XYb1157LbNnz3ZEbFLDlu7/gn1ZB3hy+BxCfILKnSs1WXnglZ8pLrFw3TAldSIilZk/fz7F\nxcUsWLCAp556CrPZzCOPPOLssC7akOYDnR2CiIhchCpH7AYMGEDnzp3ZtWsXVquV+fPnEx4e7ojY\npAYlndlL0pm9zO19D81DGpORkV92zmyx8eLnu2gaEcjdk+OV1ImInENSUhJfffVV2et58+YxevRo\nJ0Z0aca0vpKfTm5ydhgiInKBqhyxu+KKK3jppZcIDQ1l6NChSurqgMziLN5O+ogJba8iyDvQ7vx7\n3+8jM7eEW8Z2VFInIlIFwzDIy8sre52Xl4eHh4cTI7o0Ph7emG0WjuYdd3YoIiJyAaocsfv6669Z\nuXIlzz77LGlpaYwZM4Zx48YRHR3tiPikmhWYCnkh4VX6N+lN70bd7c7/tCuVDbtP8e/bB+Dv6+WE\nCEVEXMsNN9zA5MmTGTJkCIZhsHbtWm655RZnh3XJ8kz5VRcSEZFao8rELiQkhClTpjBlyhR2797N\nI488wssvv8yePXscEZ9UI6vNygs7XiPIO4gJbeynCaVlFfH2ir3cN70roUE+TohQRMT1TJo0ibi4\nOLZu3YrNZmPRokXExMQ4O6xL5uPh7ewQRETkAlSZ2GVlZfHtt9+yYsUKcnNzGTNmDC+++KIjYpNq\n9tXh7/By9+LubrfYTbG0WG0seH87Y/q3pGPLMCdFKCLiOtauXcvgwYP54osvAAgICAAgOTmZ5ORk\nJkyY4MzwLlmAV4CzQxARkQtQZWI3fvx4Ro0axZw5c4iLi3NETFIDdmfuYfXxdTzS9368/+evsIZh\n8MGq/bSICmLioNZOilBExLXs3r2bwYMHs3nz5grPu3Ji1zyoKR7ax05ExKVUmditW7dO+9a5uBJL\nKUv2fcFNnf9EpH+E3fnlPx1m58FM5t3QywnRiYi4prvuuguAJ598suxYfn4+p0+fpl27ds4Kq1qc\nyD/Jf3e/x+MDHnR2KCIicp4qTewmTpzIsmXL6Nix/MqIhmHg5uZGcnKyQwKUS/fa7neI8o+gW4T9\niGtGTjHvf5fMbRPjaBCo5+pERC7UJ598wvbt23nggQeYMGECAQEBjB8/nlmzZjk7tEuSXZrj7BBE\nROQCVJrYLVu2DIC9e/fanTOZTDUXkVSrH46v53DuUf7Zb67dc3WGYfDG13sYc1lrOum5OhGRi/LR\nRx+xePFivv76a4YOHcpDDz3E1KlTXTKx6xrR2dkhiIjIRapyjuW0adPKvbbZbEyaNKnGApLq83Pq\nVpYd/IY7ut5MiE+Q3fnNe9LIzCvhmhGuv3qbiIgzRUZGsm7dOq644go8PT0pLS11dkgXxcPNdfff\nExGp7ypN7K6//npiY2PZuXMnsbGxZf/i4+Np1aqVI2OUi5CYmcwHez/hjq4zadvA/v/LZLby2bpD\nDO7WFC9PdeQiIherbdu2/PWvfyUlJYV+/foxe/Zsl11szNP994k80UFNy742DIPb1zyAyWpiX9ZB\nPjuw3BnhiYjIOVQ6FfPdd98F4PHHH+cf//iHwwKSS2eymvjswHL+1GEqsWEVP8D/+jfJeHt5MLpv\nCwdHJyJStyxYsICEhATatWuHt7c348aN4/LLL3d2WBcl1CcEgCntxpNWlFF2/EDOYQBsho0XdrwG\nwOXN+hPu19DxQYqISIUqTex+25+nU6dOZXv0/JErL+Nc13156Fv8vPzoHdXN7pxhGCxZc5Dko1k8\ncXNfu+fuRETk/CxZsoRp06axePFigHLbHuzZs4c77rjDWaFdsGbW7qR4/MrAZv0AOFOSxa/pO5kW\nc7avX318HQD3rp9XVueRTU8xu9ss2oVqmxwRkdqg0sTut/15tmzZUuF5JXa10/qUn/kxZSMP97kP\nD3f7KZartqWwKek0d0/pQnCAdwVXEBGR82EYhrNDqDbu/3878NszdmtO/FTufKvgFiSdsV9MLaUg\nVYmdiEgtUWliV9H+PAUFBZw6dcrl9+epq7aeTuDzg99wZ9ebaRQQaXc+PaeYZT8dZu513YmOsl9M\nRUREzt/06dMBmDVrFuvWrWPo0KFkZWWxZs0al11k7LdZHJc368+6lJ/Ljueb88uVG9p8ED+cWM+P\nKRsZ3Pwyh8YoIiIVq3JVzE8++YQ5c+aQlZXF6NGjueuuu8qmnUjtUWAu5NMDXzGz858qfK6u1Gzl\npc93M6xHMyV1IiLV6OGHH2blypVlrzdv3swjjzzixIguxtnRR/f/vy1oE9KKbpHxZWfPFGfRMjga\ngBcHP8XV7cYAkFl8xsFxiohIZapM7D766CP+9re/le3Ps3z58nIdmNQObyd9RNsGregc3sHuXKnJ\nyrw3NuPr7cHEQZoyIyJSnRITE3nqqacACAsL45lnniEhIcHJUV0Y47fEroLnrr85vJLEM3u5NnYS\nLw15umxUr3WIFt8SEalNqkzsoO7sz1NXHcs7wd6sA1zfcbrdObPFxn+W7iDI35v7r+lWYactIiIX\nz2azkZ6eXvb6zJkzuLufV/da67i7/R63yWoCYMXR1QCE+TYoV3ZGh6kAWG1WB0UnIiLnUukzdr+p\naH+e+Pj4qqqJg1htVt5M+pDhLa7Ax8N+MZQ3vtlDXpGZ2VO74OnhmjcaIiK12axZs5g4cSI9evQA\nYOfOnTz00ENOjupCnR2xc+PsH/9O5J+0WyzFz9Ov3OsS69k/8t7141xeGvK0A2IUEZFzqTKx+21/\nnvbt25ftzzNo0CBHxCZVsNqsPJ/wKgBjWo2wO//TrlR2HjzDwr/2JSTQx9HhiYjUC2PHjqV3797s\n2LEDT09P/vGPfxAZab+AVW32+1TMs38A3JGxG4BiS0mldZoENKr5wERE5LxVOYRjNptZu3YtN954\nI+PHj+eXX37BZDI5Ijapwg8n1lNkKWZur7vttjbIyCnm/ZX7uXVCJyV1IiI1yGQysWzZMn744Qd6\n9+7N0qVLXbaf/O35uc4Nzz6v/dWhbwGYHjPRrqynuyfDos9uxJ5WmG53XkREHKvKxG7+/PmUlJSw\nYMECnnrqKSwWiwuu9lX35JnyWX74e67vOA1fT99y54pKzLz0+W56xUYS3ybcSRGKiNQP8+fPp6io\niD179uDp6cnx48d58MEHnR3WBfnfEbvfVr1cf3ITUf6RDGzar8J67RqcXZDrWH6KA6IUEZFzqXIq\nZlJSEl999VXZ63nz5jF69OgaDUqq9sn+L+nbqCfRQc3szn3x0xF8vT24/soYJ0QmIlK/JCUlsWzZ\nMtavX4+fnx9PPfUUY8eOdXZYF8TAVu71HxdRifRvWGm9IO9A4OwfG0VExLmqHLEzDIO8vLyy13l5\neXh4eJyjhtS0xMxkfk3fxfi2o+zOJRzI4IdfU7hpTEe8vfT/JCJS09zc3DCZTGXTGLOzs8u+dhX/\nm9j9kae7V6XnWgQ3B2Djyc1lx2yGjQPZh6ovOBEROS9VjtjdcMMNTJ48mSFDhgCwZs0abrnllhoP\nTCpWbClh2aEVjGgxmECvgHLncgtKWfTZbqYPbUdEA79KriAiItXp+uuv58YbbyQjI4MnnniC1atX\nc/vttzs7rAtkVHomIX1XlbXTizMx2yzM/vH3KahPDHiIBj4h1RKdiIhUrcrEbtKkScTFxbF161Zs\nNhuLFi0iJkZT/Jzl5Z1v4OXmYbcKps0wWPT5bi6Lb8yIXs2dFJ2ISP0zaNAgOnfuzObNm7Farbzy\nyivExsY6O6wLcq4Ru64Rnc9Zd3Dzy1h7YkO5pA7goY1PaBsEEREHqjSxs9lsfPrpp+zfv5/u3btz\n3XXXOTIuqcDuzD0cz0vhX4Pm262CuSU5DYvFxg2jXOtmQkTE1V133XV8++23tG3b1tmhXDTjHCN2\nV7cdc866a09sqPRciaUUX0+tzCwi4giVPmP36KOP8umnn+Ll5cXixYt58cUXHRmXVGDVsXVc1XoE\nXh7ln3c4mVnIG18nM7JPNO4u9lyHiIiri42N5YsvvuDw4cOkpqaW/XMlFY3Y9W/cmwltRtPQL+yc\ndR/v//tI3bOXP85TAx/hvh53AHDv+oc5rhUzRUQcotIRu61bt7JixQrc3NzIzs7mz3/+M3fccYcj\nY5M/2JG+m5MFqdzZdWa541abjUWf7qJHTAR9O2mzWBERR9u5cye7du3CMH4f9XJzc+OHH35wYlQX\nxnCzH7G7rsPk86ob6tsAgP6Ne+Hj4Y2PhzeBIb8/A/7U1hcAuLHjNfRs1K0aohURkYpUmtj5+PiU\nreoVGhrqcit81SXFlhI+3PsZM+Nm2I3WrfjlOL7eHvx1XCcnRSciUj+lpaXx9NNPExAQQLdu3bjv\nvvsIDg52dlgXpaIRu7wiEyazlfCQ81uMa1CzAeVeP97/QVYe+5H1J38G4K09H9E5vKOmZoqI1JBK\np2L+byLn7l7lzghSQ74/uobWDVrSIax9uePb92WwbP1h/nJVByXeIiIO9uCDDxIZGcm9996L2Wzm\nySefdHZIF83HCLQ79sxHCTzwyqbzqv/MwH/SPKhJuWOhvg2YFjOB5y5/ouzYvesfLjeyKSIi1afS\nEbvU1FTmzp1b6WtX7sBcSW5pPquO/8i8PveVO5545AyLv0zk9omdiY4KclJ0IiL1V1paGm+88QYA\nAwYMYMKECU6O6OJ54G13LC2r+Lzr+3tVPqrn5eHFS0Oe5vY1DwCQWniapoGNLzxIERE5p0oTuzlz\n5pR73bt37xoPRuwt2b+MXlHdiAqILDtmtdl459u9/HlkLD1iIs9RW0REaoqXl1e5r//42vX8PoqW\nnV/Kr/szsFjLT898+9u9HE/LZ94NvS7qHX5L7hZs+Q+LBi/E3U0zgUREqlOlid3EiRMdGYdUYNOp\nbezLOsATA/5R7vjqbSkE+nkzIE6LpYiI1BauPCX+j9sdrE1I4eufj9mVWb/z0lf6bOgbypmSbD7Z\n/yXTYnSfISJSnfTnslpsyb5lXBM7qdyD5ll5JSxZc5BJV7R26ZsIERFXd+DAAYYOHVr277fXQ4YM\nYejQoc4O76KVmKxlX3t7ubNx9yn2HssuO/aXhWtIOpp1Udee3//sIx3rT27CarNWUVpERC5EpSN2\n4lybTm0DoEdkl3LHl649yBVdm9C5VUNnhCUiIv/v+++/d3YI1ej3EbvV237fd85ktvHGN8l2pdds\nT6FTy3Pvb1eZP3WYyvvJS7nrx7k8PfBRArz8L+o6IiJS3nkldkVFRRw/fpyYmBiKi4vx99cv4Zp0\nIj+V95OXcluXm8qNyv2y5zRbktN54e6BToxOREQAmjZt6uwQqk2gEYm3m+95l78s7uIXP4lr2KHs\n619ObWNo9KCLvpaIiPyuyqmYmzZtYvz48dx2221kZmYyePBgNmzY4IjY6qUSSynv7vmYUS2H0qlh\nTNnxgmIzb36zl2lD2hLo58oP6IuISG0TYjThmqZ3nVfZ4ABvrLaL37Ig0DuARYMXAvD5wa8v+joi\nIlJelYnds88+y4cffkhwcDARERF88MEHPP3001Ve2GazMW/ePKZNm8aMGTM4dsz+QWybzcbMmTP5\n6KOPLi76Oujd5CX4e/kxquWwcse/2niElo2DGNGruZMiExGR6uCq/WOHFqG8OWcIeYUmXv4ikV2H\nzlz0tdzd3JnQZjQAJquZJ7c8h9lmqa5QRUTqpSoTO5vNRkRERNnrtm3bnteFV69ejclkYsmSJdx7\n770sXLjQrsxzzz1Hbm7uBYRbt+3PPsTOjERuifszHu4eZcczc4tZs/0kky9vowVTRERcnKv0j00j\nAgDoFXt2W53GDcs/hvHi57su6fq9G/UA4J51D5FSkEq+Kf+SriciUt9Vmdg1atSItWvX4ubmRl5e\nHq+88gpNmjSp8sLbt29n4MCzz4J17dqVxMTEcue/++473Nzc/o+9O4+LstofOP6ZFZgFGGDYVxEE\nccFds0wxray8t+1e23VD9asAACAASURBVG9WdjXtp1Zm3RbbzFYrs1u2121PS7NdRVOzFHdRcEFW\nWYZ9Z2Bmfn9gY4S7wKB8369Xr3jOs5zvHAfOfOc8zzmMGCH31v9hTe56ru5+eYsHye0OB68u3smY\nQaHEhnm7MDohhBBtobP3j/7ezYuNG9ybb/sf3juQxyYO5tqRzV/sXjMyGoAmm4OJ81addj1ebkYm\n97nVuf1J+pLTvpYQQoiTmDzl8ccf56mnniI/P5+LLrqIoUOH8vjjj5/wwtXV1RgMBue2SqWiqakJ\ntVrN3r17Wb58Oa+88goLFy48qUBNJh1qterEB56A2Ww842u0h4JqC3vK9nH3+f/CoNU7yz/+MY1G\nu4M7r05Eo26/1Sk6a7u4krRJa9ImrUmbtCZtcnxt3T/CmfeR7oef3TabjTTZmxcmT88pByAqzIdu\nIV7OY2+8LIEvVx9wbhs9PXB3O71JtkeZBzMqfjD/+Gwyu0vSO+V7pzPG5GrSJq1JmxydtEtr7dkm\nJ/xL7Ovry4svvnjKFzYYDNTU1Di37XY7anVzdV9//TWFhYXccsst5OXlodFoCAkJOe63k2Vltacc\nw1+ZzUYsls55q8fbOz5jRPAw6irs1NEco6W8jq9W7+fxiYMpL6s5wRVOX2duF1eRNmlN2qQ1aZPW\n2qpNzuUPA23dP8KZ95H1dY0AWCxV1Dc0ry9366VxrNuZj1bhaPVvOufWQcx5dxMAyRuzGHj4ds3T\nNbXv7by6/S3u+e5JcqrymH/hU2hVrp8oTH7HW5M2aU3a5OikXVprizY5Xv94wsQuKSnpqM91rVy5\n8rjn9e/fn+TkZMaNG8e2bduIjY117ps1a5bz5wULFuDn59elb8ncU7KX/eUHuSXhuhblH/yYzvBe\nQfgdvi1GCCHE2a+z94+1Dc2TmBh1Wh64ccBRjwkPMPLO7CQmzltFY5P9jOuM921ug5yqPKD5ubs7\ne99CkD6QLUXbSQq7AE0nSPSEEKIzO2Fi9+GHHzp/bmpq4ueff8ZqtZ7wwmPGjGH9+vVMmDABh8PB\n3LlzeffddwkPD2f06NFnFvU5pNHWyJL9y7my+2W4qbTO8rSsMnKKqrn76t4ujE4IIURb68z9o81+\nJEnTak58+3/faF88TvM2zL8KMQTRyzeen7NXY3fYeWPn+859OVV53N77pjapRwghzlUn/Gv81wVY\nb7/9dq666iqmTJly3POUSmWrZ/Gio6NbHTdt2rSTifOc9dr2d9BrdAwJOvKtaJPNzhvfpHLpkHA0\nbfBcoRBCiM6jM/eP1sYjiV14wIlvh1UqFVibbG1S94ODZwAwPvoSknPW8eW+ZYyLvIgfslax1bKT\nl7cu4urulxNqPPEEbkII0RWdMLHbtGmT82eHw8G+fftoaGho16C6ivV5v3OwMpu5wx9CqTjyzeiG\n1AJwIGvWCSGE6FDp2c0Tprw1axRK5YmX18krruH1pakMjg9o0zhGhZ3PqLDzARgdfiH3/PIwe8v2\n8/Sml+jmFcmM/v9u0W8KIYQ4icTulVdecf6sUCgwmUxHXXNHnJriuhI+37eU6f3uRKc58gxdXUMT\n736Xxl1X9pI164QQQnSoVxY3r013MkkdQFFZXXuGA4C72o2FSc+y/tDvfJy2mIyKTKYlz2Zh0rPt\nXrcQQpxNTpjYjRs3juuuu+5Eh4lT9PWB7zkvaBBRXhEtyrfvLyY21IsBPc5shjEhhBDiVHy7/uBp\nn+twONr9y8jhwUMYGjiQ+Vte52BlFvvKMogxdWvXOoUQ4mxywvsYPvroo46Io0vJqsxhb+l+Lo26\nqEW5w+Fg6bqDXJgYcowzhRBCiPaRU3jqU3C/Mzup+dyi6rYO56hUShX3DrwLgJe2vt4hdQohxNni\nhCN2gYGB3HzzzfTt2xc3Nzdn+dSpU9s1sHPZmtxfOS94MJ7alg+m/76nkLLqBoYktO2zCkIIIUR7\nmvPuJhZMvwC9e8csSTB70HTmbXqJzMpsIj3DO6ROIYTo7E44YpeYmMjgwYNbJHXi9BXXlbKxYAsX\nhAxrUV5d18iHP+7ln0kxKOXZOiGEEGeZTWlFHVZX2OGZMZ9LeRWrrbHD6hVCiM7smCN2X331FVde\neaWMzLWxtXkb6OffG18PU4vy7zZk0SPMm5GJMo2zEEII14kN9Tql4/9YqLyq5sRr3LalC0OHsyZ3\nPTPW/IdnLngUg0bfofULIURnc8wRuw8++KAj4+gScqsOsTL7Fy6PGtuiPLOgkh82ZnPdRTEyE6YQ\nQgiXio/0Oa3zvlp7ELvd0cbRHNs/Yv/Gw0PuAeD+tY9x16pZ1Da2/yydQgjRWckiMB3E4XDwUdoX\nXBY1lgB9yxkvv157kL+dH4XZ2+MYZwshhBAd42/nR532uf/7Kb0NIzmxQH0ADx1O7gDuW/soZfXl\nHRqDEEJ0Fse8FXPfvn2MHj26VfkfUxqvXLmyXQM716SWpJFdlce9A1re2rons5QDeRVM/lsvF0Um\nhBBCnJkXpw5n5qvrWb3tEKMHhBJiNnRY3UH6ABYmPUtFQyUPrn+SxfuXc3uvGzusfiGE6CyOmdhF\nRESwaNGijozlnPZD5irGd7sElVLlLKtraOJ/P+9lwugY3LSq45wthBBCdF7eBjdGDwhl5eZcHn57\nI2/fP6rDHy3wcvMk1tSdrUU7OrReIYToLI55K6ZGoyEkJOSY/4mTl1a6j7zqQ1wYel6L8g9/SsfX\n053zegW6KDIhhBCibdwwJhb3w19S3vZMMnnFNR0ew9DAAQC8tfPDDq9bCCFc7ZiJXf/+/TsyjnOW\nw+Hg24M/MSZiJO5qd2d5Y5ONTXuKuGZktEyYIoQQ4pzw5O1DnD8//NbvNDTaOrT+IUHNid1Wy05s\n9o6tWwghXO2Yid0jjzzSkXGcszIqsrDUlXBxRFKL8pR0CxGBRsIDjMc4UwghhDi7+Hi6s3DGCOf2\n0rUHOzyGZy+YA8Ddqx/o8LqFEMKVZFbMdrYhfxNDAwe2eLbO7nDw3YYskvrLLa1CCCFcz+Fou2UK\nPNzUvHz3+QD8sDGbifNWsT+3os2ufyJ6jY4b4q4BYPG+bzqsXiGEcDVJ7NqR1WZla9EOhgUPcpY5\nHA4WLUvFAQztKc/WCSGEcD374cTOx9OtTa5n1Gl58MYBzu25/9vMxHmruPe19W1y/RM5L3gwfc29\nWJWzls/Sv2rTxFUIITorSeza0aqcdUR6hhOgMzvL9udVkHqwlJn/6ItSKc/WCSGEcD27vfn/j08c\ncvwDT0H3UC9MRjdMxiPJYmllAxPnreKrXzLarJ5j+WPU7pe8DUxNvp9vMn6kvqm+3esVQghXOeZy\nB+LM2B12VmavYWri7S3K1+3Ip1c3X3w83Y9xphBCCNGxbIczO2Ubf937wl3DnT/b7Q5ufzYZgN93\nF3LliG5tW9lf6DU6FiY9S07VIeZteokfMlfyQ2bzGryPDLmXAL1/u9YvhBAdTUbs2sk2yy583U2E\nG0OdZZU1VtbvLODvF0S5MDIhhBCiJZu9+VZFZTvO0qxUKnhndhIqpYKi8jomzlvFxHmr+ODH9Har\nEyDMGMxLI+cyd/hDeLt5AfD4789z16pZZFfmtmvdQgjRkSSxayfJOeuI84ltsZTB0nUH6dXNhwCT\nzoWRCSGEEC3ZbIcTuw54ROC+6/q12F69NY8Dh9p3chWNUo2XmydPDf8Pz10wh16+8QA8t/nVdq1X\nCCE6kiR27cBmt5FRkcnw4CPPKuSX1JC8NY/rRse4MDIhhBCitY4YsftDbJg378xO4q37R/H8lPMA\neOqD5slVJr+whrqGpnatX6fRMbnvrYwMHY7dYeeuVbPYW3agXesUQoiOIIldO1h64HuiPMMx63yd\nZWu2HWJ470ACfGS0TgghROfSZGt+xq4D8jonpUKBj6c7d13Zy1nW0Gjjrvm/UG9t3+QO4NrYv3F5\n1FgAXt76BlNX3c/Ggi0yg6YQ4qwlk6e0sYqGKtbk/coDg6a3KE/LKuP6MbEuikoIIYQ4tiOJXcfP\n1jyghz/vzE7C4XAw/4vt7MooZcqLv/DO7KR2r/vSqIsYGXY+D6x7gkZ7I+/v/pT3d3/a4pib4/9J\nT98eGLWGdo9HCCHOhCR2bSw5Zy2J5l4E/mm2rYP5lZRU1hMVZHRhZEIIIcTRNTbZXR0CCoWCmf9I\n5LWvd5GSVkRKWhGJMX6oVe17c5GH2p2XRj4FQEVDJQ+uf7LF/g/2fAZAlGc49wy4yyXJrxBCnAxJ\n7NqQw+FgY8EWboi/pkXZG8tSuWRIOBq1yoXRCSGEEEfXGRK7P9x8cQ9S0op47etdzrIZ/+hL726+\nxzmrbXi5ebIw6Vnntp+fAYulim8yfuTHrFVMTb6fF0Y8Tl1TPV5unigV8kSLEKLzkL9IbWhXyR7c\nVFrifY7ccrnrYCmllfVcOiTChZEJIYQQx9Zo6zyJncFDw8IZI0iINDnL5n++3SWxKBQKFAoF46Mv\nYVLvWwC455dHeOjXuUxLnu2SmIQQ4lgksWtDK7LXMDp8RItv8JK35HHFeZEdMoW0EEIIcTryS2pd\nHUILHm5q7pnQj3dmJ3HtyGgA3lq+myabnYnzVvH28t3ORdU7Sl9zAi9d+BQ3xv+Dv0VfCsBdq2Zh\ntTV2aBxCCHEscitmGzlQnklWZQ539b3dWZZ6sJSswipuuzzehZEJIYQQZ69Lh0bwxeoD/LqrgF93\nFQCwflcB63cVsHDGCDRqZbs/h/cHjUrDsKCBAHhpPflgz2fMWPMfAFQKFTfGX8sA/76olPLohRCi\n40li10bW5v3GBSHD0Ko0zrLkrXlcfl4kenfNcc4UQgghxPH8954LyS+pYUVKLteO6s5/v97F3pxy\n7pr/S4vjXps5Andtx3y0GRI0gCFBA9hStIO3d/0Pm8PWalbNQH0Ak3rdRIDeH7vDLs/kCSHalSR2\nbWRT4RZmDZzm3LaU17Enq4x/XRrnwqiEEEKIs5+bRkVkoCe3X94TgPuv78fenHLUKiVLfslgT1YZ\nAFNe/IUF0y/AZnPgqdd2SGz9/fvQ/08TrhTVFmOpK2Z5xo9kV+Xx+O/PO/fdmnA9AwMSOyQuIUTX\nI4ldG9hatBOtSku4MdRZtnprHoPj/TF4yGidEEII0ZYUCgU9wpsnV7nvun4ArNycy0c/72XaS2ud\nx/mbPHjqjiGolB03Uuav88Nf50eCb/MXu4eqC0gp3Mb24lTeTf2Yd1M/Zmb/KUR7R3ZYTEKIrkES\nuzbw7cGf+Gfs351r2xRX1LFqSx7/uXmAiyMTQgghuobRA0Lx1GsxGdyY+7/NABSV1XHHs6t5+/5R\nLlt/LtgQyHjDJVzebSyP//YclroSXtzymnP/Fd0uJinsArSqjhlhFEKcuySxO0N51fmU1VcwwL+v\ns+zbDVmc3yeIULPBhZEJIYQQXcugOH8A3pmdBMCSXzJY/msmtz2TzMAeZqZc2dtlsSkVSuYMux+A\nT9IWs6d0LyX1ZXyT8SPfZPwIwPU9rmZ4yBCXxSiEOLtJYneGfsn9lWHBA9EcnjSlssbKxj2FPHn7\nUBdHJoQQQnRtV43oxuj+Icx4dT0p6RayC6sIDzC6Oiyui7va+bPV1sh7uz8hsyKbj9MX83H6Yue+\nAf59uTH+WhnNE0KcFEnszoDD4SClcDvT+9/pLFu9LY/E7mZMRjcXRiaEEEIIAC+DG4vuG8mk51Yz\n591NmL3defrOYShddGvmX2lVGib1vhmA1JJ0fN1NfJ+5gpTCbWwu2s7moubF2Xv79STOFMOw4EG4\nSaInhDgKSezOwJai7biptIQZQwDIKqhi+a9Z3H9DPxdHJoQQQog/qFVK3pmdxKtLdrJlr4Xbn0km\nqX8I5/cJ4vH3UhgU58/fzo/Cy6B16RJFCb49gObZM29NuB6Hw0FyzloW71/OzuLd7CzezRf7lmLQ\n6HnivAdkJE8I0YIkdmdgde56rup+mXP72w2ZDI73JzrYy3VBCSGEEOKopl7Vm4ZGG5NfWMOqLXms\n2pIHwKa0IjalFQHNt2+OGxbRKUb0FAoFSeEjSAofAUBNYy2z1s6hurGGGWsech4XbgzF5O7NJZFJ\nLWboFkJ0LZLYnaZfD22iqLaYvv7ND2LX1jey40AJD90y0MWRCSGEEOJY3DQq3r5/FDa7g9qGJjx1\nWuwOBwUltTz01u8s+SWDJb9kOI+/d0IiPSN9XBjxEXqNjoVJz1LRUEVyzlqK60tRKZTsLN5NbvUh\ntlt2OY/18/Clu1cUV8dcgU7j4cKohRAdRRK707TNspMrul2MRtnchNv2FxMT5i0zYQohhBCdnEKh\nQK1S4KlrvpVRqVAQ7KfnndlJlFbW8+1vWSQfHs17/tNtx7zOE7cPIcRP3yEx/5mXm5G/dx/XqnxX\n8R5+y0+hurGGfeUZFNeV8FtBCgBxphhu7XU9Bk3HxyuE6BiS2J2G+qYGdpekc12Pq5xlP6fkMqpf\niAujEkIIIcSZ8vF056axPbhpbA98fQ188XMaq7ceorrOSpPNQXVdo/PYh9/6vcW53gYtw3oFcu3I\n7h0dNgC9/OLp5Rfv3HY4HOwoTmXRzg9IK9vH/WsfAyBA58/DQ+5x2dp+Qoj2IYndadhm2UmsKRqT\nuzcARWW1ZBdWMSwhwMWRCSGEEKKtKJUKkvqHktT/6M+tFZbV8r+f9hIV5MnKzbmUV1v5/rdsvv8t\n23mMr6cb/WLN+Hm6M6p/CGqVssMSKoVCQV9zLxYmPQtAlbWahdvfJqcqj6nJzWvq+br7cFP8tYQa\nQ3BXuUmyJ8RZTBK70/DroY0MCuzv3F65OY8xA8PQqFUujEoIIYQQHSnApOOefyYCzZOuAJRVNbAr\no4TNey3sOFBCSWUDK1JyAfh01X4Axg4K459J3Ts8iTJqDcwe9H+U1pfx9f7vSC/bT0l9KS9tfcN5\njFqhYtaguwkxBHVobEKIMyeJ3Smqb6onqyqXqYm3A9Bks7NxTyHTr+3r4siEEEII4WomoxsX9A3m\ngr7BLcodDgdlVQ2s35nPV2sP8tOmHOe+O8cnMKCHGbVK2SEx+ribmNjrhhZltY11PLDucZocNuZu\nnA/A3YmT6OHjmttKhRCnThK7U7TNsotwY6hz7ZhVm3Px83InItDo4siEEEII0VkpFAp8PN25YngU\nlwyJIDWzlA9+SKO82soby1Kdx/WL8aN3N19CzQa6h3bc8kk6jQcvj3oaaF7O6Yu9S3ll2yLifWIZ\nGzGKWFN0h8UihDg9ktidou2WVBLNvYDmb982phUx4i/fygkhhBBnE6NOQ1Vt44kPFG1Co1aS2N2P\nxKnnA2BttHHPwvXU1DexM6OErfuKncfq3NQoFHD/9f0J9e+YmbdHhg5nZOhwvti7lOyqXF4+fKtm\noM6fPsFxOKxK4nxi6GHq+NtJhRDHJondKahrqmN3aToTelwJQK6lhpLKes7rFejiyIQQQojT53C4\nOoKuTatRsWD6iBZlVbVWnvtkG4VltTQ22XnknY2tzvP1dOOakd2JDvHEz6vt16q7NvZvAFhtjazO\nWce6Q7+zPjuFGmstP2evBiDaK5LJfSfioXZv8/qFEKdGErtTkFqcRoghCC83TwA2pRXRp5tvh90T\nL4QQQoiuwajT8vhtg4HmO4QKSmtZkZKLm0bFgDgzqQdL+XrtwRa3cQKE+Rt49NZBKNtwJE2r0jA2\nchRjI0dhNhuxWKqwO+ws2bec5Nx13PvLIwD8u8+/sDsc9PKNQ6WUCeWE6GiS2J2Cn7PXMDq8+Ru1\nxiY763Yc4s7xCS6OSgghhDgzDhmy69QUCgVBvnpuuriHsyw62Ivxw6OA5pk4cy3VaFRKnv1kK7c/\nkwyAVqNEq1bhcDiYdnUfYsO82ywmpULJNbHjuSZ2PL8e2shHaV/y+o73WhxzRbeL0Wv0DA0aiEYp\nHzmFaG/yW3aSdhXvobiulERzbwBWb81D76GhR7jJxZEJIYQQoiszGd0wGd0AeGd2Er/tLuDgoSrK\nqxtw06rYtq+YeR9tAZqf2YuLMDFmYCixYd5t8ozcecGDOS+4eXTRarOSUZHF0gPf803GjwB8mr4E\nAH+dH9FeUfjr/Ijx7kaEZxhKhdz1JERbkcTuJP1WsJmLI0ehVWkA2JxexMjEEBdHJYQQQpw5GbA7\ntwztGcjQni2f/7c22njzm90UlNay40AJW/ZanPuGJQTSPdSL7iFe2Ox2dG5q/E2606pbq9IS5xND\nnE+Ms6y+qZ4V2WsorLXwW34KDo684SKMYZwfMpQE3zi83GSGcSHOhCR2J6HR1sj+8gzGho8EoN7a\nxIFDlUyS2zCFEEKcAySvO/dpNSruuqq3c9vhcJCSbuGL5P1sSC1gQ2pBq3PUKiV9on0J9tNz2dAI\ntJrTG11zV7tzebeLW5TlVOXxSfoSsitz+TR9CTaHzbkvWB/IjP7/Rqc5veRSiK5KEruTsLloO2YP\nP8I9Q4HmSVNCzQZ8PGUGKCGEEOcCSe26GoVCwaA4fwbF+bcodzgc/Lgxh8YmG+t3FlBcUceWvRaW\n/5oJQGy4N146LbddFo9Wc/oTpIQZQ5g1cJpz2+6ws6d0L9uKdvJr/ibuWzsHgACdPwpgSOAAevnF\nE2yQmciFOJZ2S+zsdjtz5swhPT0drVbLk08+SUREhHP/e++9x7fffgvAhRdeyNSpU9srlDO2InsN\nl0eNdW7vzCilT7SvCyMSQghxtuqM/aPciin+oFAouGRIOABXHJ6cBcBmt7M/t4I3l+9mb3Y5m9KK\nnPsMHhquGB5JfISJQB/dac0WrlQoSfCNI8E3jhvir6WgppDtllSsNis/Za9macb3LM34HoNGT2+/\nnowJv5AAvf+JLyxEF9Juid2KFSuwWq189tlnbNu2jXnz5vHf//4XgJycHJYtW8YXX3yBQqHg+uuv\n56KLLiIuLq69wjltpfVl5NcU0ssvHoDy6ga2pFt4dvIwF0cmhBDibNQZ+0fJ68SJqJRKeoSbeP/R\nS7BYqiiuqCO7sJpfth9iX24FX64+QGOT3Xm8Vq3E18udaVf3IdDn1G+pDNQHEKgPAOCK6EsAKK4r\n4YnfnmdD/iY25G9yHjsmfCSXdRsrM2+KLq/dfgM2b97MBRdcAEBiYiK7du1y7gsMDOStt95CpWoe\nwm9qasLNza29Qjkjmwu3E2oIRn34j8WqLXn072GW2zCFEEKclk7ZP0pmJ06Rn5cHfl4e9I81tyjP\nKapmb045GYcq+W13AQ8u+g29u5o+0b6YvT24/LzI017/18/Dl5dHPQ1Ao72J0rpSVueu5+fs1c4F\n0z3UHnTziiDOJ4YB/okyIYvoUtotsauursZgMDi3VSoVTU1NqNVqNBoNPj4+OBwOnn32WXr27ElU\nVNRxrgYmkw61+swXuzSbT+0XPGtPFv1De2E2Gw8/aFzExCsSTvk6nd259nragrRJa9ImrUmbtCZt\ncnxt3T9CG/SRh2e8l3+71qRNWjtem5jNRvonBDm3M/MrWZK8D0t5HRvWZ7JsfaZzn0qp4P6bB9E7\n2heDTnvKcQRjoldkNFO5mVprHa+n/I/K+ipSLWmklqSxeN83APQJiGds9xH0DeyJm/rU6zkZ8j45\nOmmX1tqzTdotsTMYDNTU1Di37XY7avWR6hoaGnjwwQfR6/U8+uijJ7xeWVntGcdkNhuxWKpO+niH\nw0Ga5QBjQ0djsVSxN6ecxkYb3fwNp3Sdzu5U26UrkDZpTdqkNWmT1tqqTc7lDwNt3T/CmfeRDnvz\nkJ28n1uS3/HWTrVN9GoFN42JBcBud5BZUEVFTQPfbsgi41Alc9/b2OL4EX2DiAn1pn+sGQ+3U/uY\nelPMhBbbldYqlmf8RH1TPc+vf6PFvut7XM2gwP7OZazOhLxPjk7apbW2aJPj9Y/tltj179+f5ORk\nxo0bx7Zt24iNjXXuczgcTJkyhSFDhjBp0qT2CuGM5VTnoVaoCTUEA/DjxmyS+oeiVJ75Yp5CCCG6\nps7YP8qdmKIjKJUKugV7AtAv5sgtnLX1jazYnEvqwVJ+2Z7PL9vzefvbPQDEhXtz6dAIenc79Unr\nPLVGro+7GoCJ3IDdYedAeSaf7/2aj9MX83H6YuexGqWGYH0gYcZgevr2oJtXJAaNvk0WcBeio7Rb\nYjdmzBjWr1/PhAkTcDgczJ07l3fffZfw8HDsdjsbN27EarWydu1aAGbOnEm/fv3aK5zTssOym/4B\nfVEqlBRX1LHrYCkTL4t3dVhCCCHOYp2xf5RZMYUr6dw1jB8exfg/zcLZZLPz3693cSCvgvmfb3eW\n693VDO0ZyD+SotGc4u3HSoWSGFM3/jNkJgA2u40KayWZlTkU1hSRVZVLakk66w797jxHo1QT4x3N\nZd3GEGEMk0RPdGrtltgplUoef/zxFmXR0dHOn3fu3NleVbcJh8PB2rwNXHf4m551O/LpF+OH3v3M\nh+yFEEJ0XZ2zf5TMTnQuapWSaVf3AcDucLA3u5y1O/Ipq6pn5ZZcVm7JbXF8z0gT3gY3TEY3vPRa\nBvTwR+euxu04a+2plCp83E34uJta7bParGRV5lDeUMlv+Sk8l/IqAPE+sSSae3Fe8GCUitObBEaI\n9iLzwh5DTlUeChT09UsAYNfBUsYMDHNxVEIIIUTbkxE70ZkpFQriIkzERRxJwJpsdnYcKKG2vona\nhia0GiVb9lrIs9SQXVTFxyv2AaBWKfA2uNEryof6RhuXDYskyFeH8gQjb1qVlhhT8xcugwL74XA4\nSC1J46es1XySvoRP0pcAYPbwJdYchVHpRbgxlF6+caiUZz7ZnxCnQxK7Y/gpK5mhQQNRKBQ02exk\nHKokNszb1WEJIYQQQnR5apWy1VILIxNDWmwXl9eRW1zDjv3FZBZUkVlQxW+phQB46bUMivPn8uGR\neJ7EjJwKhYJefvFH1jVuqGB/WQabCrcBsKdkLz9mrsLxp9Fvbzcv4kwxjA4fQbAh8IxerxAnQxK7\no7DarGy17OTpjsaWJgAAIABJREFU8x8GYO2OfCIDjZiMnXOtPSGEEOJMyIidOBf5eXvg5+1BYnc/\nZ1l1XSO/pRZgKa9n7Y5DrNh85JbOCxODCfLRMTDO/4TrFXu7eTEwsB8DA/u1mOmw0d5EaX0Zu0vS\nyarMpc5Wz1MbXwRApVDRzSuCUEMwHmp3RoSeh1FrOF41QpwSSeyOYmfxbsKMIXhqm9euW7LmALdd\n3tPVYQkhhBDtwiHP2IkuwuCh4aLDj9Zcd1EM1XWNbN9fTFFZHUXldXy6aj+frtqPVqMkOtiL3t18\nCfTVkRDpg0Z94mfqNEo1ATozAbojo4k2u42S+jJ+PbSRKms1e8r2UVxbzHeZKwAIMQQxNnykc8I+\nIU6XJHZHsalwG4nm3s0/pxWhd9fQJ/rUp9kVQgghzgqS14kuyuChYXjvIwuq3zk+gcLSWtbtzCe7\nsJrPk/ejVilosh35JTEZ3VApFQT66OgR7k1spC8Rfjq0x5ioRaVU4a/z4+/dx7UodzgclNaXsyzj\ne97d/Qnv7v4Ef50fvXzjUSlU9DEn4Ofhg0Gjl4RPnBRJ7P7C4XCwryyDMeEjsdntfLJyH/8Y1f2E\nD9kKIYQQZyvJ64Q4IsBHx9UXRrcoq61vpKLGSnm1lcYmGxv3FJFVUMXyDVk0rMlwHuep1+Kp0zIs\nIYCwAAMmozvBvrqjLpOgUCjw9TBxa8L1/KvndSTnriOvOp8qaw27S9L4OXu181gPtQdeWiMRnmHN\nz/r5xqFVnfjZQNG1SGL3FznVeWhVGqK8wtlxoBSjh5ahPQNcHZYQQgghhHARnbsGnbuGIF89AH2i\njzy3ZzYbKSqqpKC0lpR0C9+sz+SXHfkUltY6jzHqNHQP8cLDTY3N7sBkcGNIzwAiAo1Ac5KXFHZB\nq3ptdhu7SvZQ1lBBZUMVedWHeHvX/5qvqTXQw9SdC0KG0d07qtW5ouuRxO4vUgq30dO3B0qFkm9/\nzWRUv2BZjFIIIYQQQhyTQqEgyFfPFefpueK8SGe5w+Eg41Al+3IrsFTUUVPXSG19E/UNTTz23ibn\ncWqVAi+9Fn+Tjrhwb8YODsdNo0KlVNHX3KtVfdXWGtYf+p2sqlzmb/kvAEaNAb1GR7hnKInm3sT7\nxMioXhcjid2f2Ow2fjuUwsReN5BnqSa3uIYRicGuDksIIYQQQpyFFAoF0SFeRId4tdrX2GSnodGG\npbyOwrJa3LVqUg+W8tXag3y19iAAnjoNwX56+kT70SvKhxCzHoVCgUGr5+LIJADsDjuZlTlkV+VS\nba0hv6aQRTvfd9YT5RlBnE8MF8osnOc8Sez+ZHtxKiZ3b+J8Yli2/iDdgjxRKeVhVSGEEEII0bY0\naiUatRKDh4aoIE8AErv7ccOYWJpsdkoq69m6t5h9ueUkb83l8+T9znP17mr8TR4kRPkS5m8gITKE\nbl4RLa5f11TH3rIMcqvyWH9oI99nrsCg0dPPvw8Jvj0I1gfi6eaJRinpwLlC/iUPczgcfL3/O5LC\nm+9v3nGghMuGRZzgLCGEEOLcIE8dCNF5qFVKAkw6LhkSziVDwoHmz6rVdY1U1lj5fU8hRWV1lFTU\nsfzXTOd5oWY9cREmwswG+vcw09ecQF9zApd1G0tdUx0phdtZk7uetXkbnOd4aT0J9wwh3qcHffx6\nYnL37uiXK9qIJHaH7S8/SE1jLcODBnOouIY8Sw1x4SZXhyWEEEJ0CMnrhOjcFAoFRp0Wo07LVeYj\nt1TecUUCVbVWUg+Wsn5nPpn5VezMKOXd79NQKhTo3NUE+uiICDAS4h/GeL9biEn0xsNNTUVDJdst\nqeRW57Eiew2f7/0avUZHX78ElEoVsd7dCDYEEaAzy5ILZwFJ7A7bW36AYcED0ag0rNicwch+wXi4\nSfMIIYToImTIToizllGnZWhCIEMTAp1ldQ1N7Mkqo6K6AUtFPQUltezKLHXO1mnUaQjx0zMsIYLR\nYX2Z0MODSmslu4r3UNNYy5rc9fyWn0KTvQmAWFN3YryjMLmb8HP3Ido7UpK9TkYyl8P2lKRzUfiF\nOBwOUg+W8O+/tZ6BSAghhDhXSVonxLnFw01N/1jzUfcVltaSkV9JVkEVP23K4d3v0wCIDDTSM9LM\n+X2CuPj8JOfx+TWFbC7czoHyTApqN1LRUIkDBx5qDwJ1ZrwP374Z7xNDgM6faK/Idn99ojVJ7ID6\npgYOVmYT79uD/JJabHYHkYfXFRFCCCG6AhmwE6LrCPDREeCjY9jhET6Hw8G+3AqW/5rJ9v3FfPdb\nFtA8Scu4YRHNE7R4DGWY7wh8PN1QKBRUN9ZQVFvMgfKDZFbmYLVZ2WHZzd7yb7DarHi5e+KhdGd4\nyBCC9YHEmqJlhK+dSWIHbCnaTk+fHriptOw6WEhsqLesXSeEEKKLkX5PiK5KoVAQG+bNzH8mAmB3\nONi4p5DdB8vYuq+YtdvzaWi0UVbVAECYv4Fe3XwI9zcSpu9Lv7Ah+Hl7OK+XW3WIWnUlGzN3srsk\nncX7vgEgxBCE2cOXoUEDifGOxl3t1vEv9hwmiR2wIT+FpLALcDgcrNmWx7Wjurs6JCGEEKJDyfeZ\nQog/KBUKhvYMZGjPwBblDoeDQyW1bN9fTGZBFb/vLqS0ssG5PyrIyPm9g/D18qBXbAKx8XHOfWX1\n5ewq2cOu4jTeTf2YBpsVAD93H8I9Q+njl0CMqRteWk8ZYDlNXT6xq2uqI7cqjwTfHpRVNVBW1UCf\nbr6uDksIIYToUPIxSghxIgqFghA/PSF++hbl1kYbv+0uJD27nPW7CmhotPHSF9sBMHu74+flgcPh\noF9sKEn+cUzs6YlWraSw1kJmZTbJOet4b/cnzusZNHrMHr4EG4LwcvMk1BBMd+8odGoPSfqOo8sn\ndgfKMwkzhqBVaTmYX0RkoBGlUt4wQgghuhj5sCSEOE1ajYoRfYMZ0TfYWebnZ2DTzkNYyutIPVhK\ncUU9323IoqLG6jwm2E9PtyBPhgVfy7DzA9FqlNQ01pJetp/8mgIqGqr4PT+FHxoqsDvszdd19yHS\nK5yRocMJMQSjUaol2TtMEruKTCI8wwDYlFZEn2g/F0ckhBBCdDz5XCSEaEsKhYKoIE+igjwZHB/Q\nYl9dQxMH8ytZkZJLYVktqZmlfPBjOnr35iTNU68lwBTEsIR+jE8cj1GnBaCgppDtllSyq/J4fvNC\n5/V83X0waPTE+8bSwxRNN69I1Mqul+Z0vVf8F+ll+xkRMozCslpS0ixMGB3j6pCEEEKIDid5nRCi\no3i4qekZ6UPPSB9nWXVdI5byOqpqrdQ2NLE53cKby3fT2GRH764mxGwgOsQTqzWUwd36cFmfqzB7\ne2DDynZLKpa6YgpqLfyYuQoHDgwaPT1M3Qk1BBOo9yfKKwKj1nCcqM5+XT6xy68uoIepO79vL2Zg\nnBlvg8zOI4QQouuRETshhCsZPDQYPDTO7T8mbmlssnOouIZ9ueXkl9Riqahjd3Ip5dUN1DXYAIgJ\n9cJkDCHYN5YH+1yJyqOGnKo8SuvLyavJZ1PhVg7VFOCm0mLUGknwjaOHKZoepu64q91d8nrbQ5dO\n7Kqs1Thw4O3mRWpmpkyaIoQQoguTzE4I0flo1EoiAo1EHGWN6dr6Rg4cqqSmrpEcSzXrdubz9bqD\nQPMafKFmLzz1Zrp7DmJMkBGFoYzc+oMU1xfz3u5Psdqs6NQeRHtH4eNuort3FInmXmftentdOrFL\nLUkjUB9ARY2V3ZmlTBwX7+qQhBBCCJeQETshxNlG566h9+GBmaHAtSO743A4KK+2UlxRR1VtI3nF\nNeQX1/D60t2Hz3InNjSBnl4DCQ5UU6cupFqZx/7yDNbn/cbbDhtGrYFY72iCDYGcHzIUg0Z/zBg6\nky6d2B2qKSDcGMK6HfkM7RmIySi3YQohhOiaJK8TQpwLFAoFJqOb83N9/1gzAJPGJwBQXt1AWnYZ\nB3Ir2Z9Zx/YDjYA/4I+Pp5aBcZ4oNOWU1WaxvXYP32T8CICPu4lRocPpY+6Fn4fP0ap2uS6d2OVX\nFxLvG8v634u5dEi4q8MRQgghXEeG7IQQXYC3wa3V4uuVtVa27rWQWVBFbbUNS66O3KIwrE0hoIjD\nFFKOZ6iVDZlpLN6/HJ3ag3ifWIYGDSTWFN1pZuDsHFG4QJO9iT2lexkfdRmfFu0hIapzZt5CCCFE\nR5C0TgjRVXnqtFyYGMKFfyl3OBxkFlSxO7OUfbkVHCqsot4aQoN7DVsDLGwt/BC7wkq0ZzdifKLo\n79+HEEOQS14DdOHErrDWgo+7ieIiFSFmAx5uXbYphBBCCBmwE0KIv/jzWnx/VlRWy/YDJRwqqWFH\ndhZpblnkmNL4IXMlSpTE+cQQ492N4SFD0Gt0HRZvl81msqvyCDEEsX1/CYPi/F0djhBCCOFiktkJ\nIcTJ8DfpGDPwj4QtjspaKylpRezNLSMlfyc7vYtJ80lmacb3dNf35J/xlxHsaW73uLpsYrfTkkq0\ndyTLV1uY/Pderg5HCCGEcCkZsRNCiNPjqdOS1D+UpP6hNDb15GB+FVmFVaRk72V/9SaeqnkOR72e\neRfPwlPl0W5xdN3ErmQP/X2GUFl7iG7Bnic+QQghhDiHSWInhBBnTqNWERvmTWyYN2MGhgGjKakr\nY+n2jejd3KCp/eo+O1ffO0NV1mrsDjv52VoGx/ujUatcHZIQQgghhBDiHOTrYWLi0IsJNHm1az1d\nMrHLqswhzhRDWlZFq4chhRBCiK5IIc/YCSHEWa1LJnb5NYUE6v3Js1STGOPn6nCEEEII15O8Tggh\nzmpdMrErrLWgwxsH4O/dfg8wCiGEEGcLyeuEEOLs1iUnT8mvKcTNEUF0sDsKeVpcCCGEkMlThBDi\nLNflRuwcDgfZVbnk5SjoHe3r6nCEEEKITkIyOyGEOJt1uRG78oYK7A47+7JquWm0JHZCuNKWLSk8\n8sgDREZGoVAoqKmpITg4hEcffRKNRnPa13300Qf429+upn//gWcc43fffcNbb71OcHCIs2zChBs4\n//wLz/jaf7Zt2xYMBiPdu8e06XXbyrp1v/Dee2+hUqm47LLxjB9/ZYv9jz76ACUlJWi1arKzc0hI\n6MVjjz3NG28sJCVlIwqFgunT76VnT1k3tLOSETshOg/pH4842/vHffvSmT//OZRKJQaDjlmzHsbH\nx5dly75i6dIlqFQqbrnlNoYPv+CMY+lyiV1RbTEmrQ/1bmp8PN1cHY4QXd6AAQN57LGnndtz5vyH\ndevWMGrURS6MqqUxYy5h8uRp7VrHt98uY/TosZ2y42pqamLBghd5880P8PDwYPLk5g7I1/fI5FN/\n/BtqtXauv/5Gpk27h71709i9exeLFr1HQUE+s2ffw/vvf+KqlyFOQPI6IToX6R+bne3948svv8CM\nGfcRE9ODlSu/5aOP3uf662/myy8/5a23PsRqtTJlym0MGjQErVZ7RvF0ucSu3laPqklHn26+8nyd\nEJ1MY2MjJSXFGI2e2Gw2nntuLkVFhVRUVDB06HncccdknnpqDhqNhoKCfEpKinnwwTn06BHH4sWf\ns3z51/j6+lFWVgY0/8F9+unHyMvLw2azMWHCDYwePZapUyfRvXssBw8ewMPDgz59+rFx4waqq6t5\n8cVX8fQ88TIoVVVVPPTQvZSVVWCz2bjjjskMGDCIm276B2FhEWg0Gu6770HmzXuciooKAKZPv4/o\n6O489dQc8vJysVqtXHfdjYSEhPH77xvYuzeNyMhuLFnyGSNHjm4xulVUVMjzz8/Dam2gsrKCf/3r\nDkaMGHlS9S1e/Blr1iTT1NSEwWDgqaeea/GN76JFr7Fjx7YWr2/+/IXOYzIzDxISEuZslz59+rJ9\n+zaSklp/uFiwYAHXXPMP/Pz88PPz44UXFqBQKCgoyMfHx+dU3g6io0mfKESndbb1j0888TBWaz31\n9dYu3z/OmTMXP7/mRM9ms6HVurFnTyq9e/dFq9Wi1WoJCQnjwIF9xMcnnPJ748+6XGKXXZVHTY2S\nntHyAUOIv3r4rd/JK65ps+uF+Ol54vYhxz1m8+YUpk6dRHl5GQqFgvHjr2LgwMHk5x8iIaE3s2c/\nTENDA1ddNY477pgMQGBgELNm/Ydly75i2bIlTJ58N1988SkffPApSqWS2267EYClSxfj5eXNww8/\nQW1tDRMn3siAAYMB6NkzgenT72XmzGm4u7vz0kuv8eSTj7Jt2xZGjBjZIsaff/6B1NSdAHh7m3jy\nyWd4//23Oe+88xg37iosliKmTLmdzz77mrq6Ov71r9uIjY3jtddeYcCAwVx55TXk5GQzd+5jvPDC\nK2zZksJbb32IQqFg48bfiIuLZ8iQYYwePZbAwECmTPm/Vu2UlZXJhAk30L//QHbu3M7bb7/BiBEj\nT1jfwoVvUlFRwUsvvYZSqWTmzKns2ZNKnz6JzmtPmjTluP9GNTU1GAwG57ZOp6emprrVcWVlpWzY\nsIG3357qLFOr1bzxxkK+/PIzZsy477j1CNeSvE6IY5P+8dT6x4EDh3DXXZPYvftAl+8f/0jqdu7c\nzv/+9z9efvkNNm7cgF7/5/N0VFe37ldPVZdL7NJK9lNRYKTnaEnshPir43UyZrMRi6Wqzev841aT\niopyZsy4i6CgYAA8PT3ZsyeVLVtS0Ov1WK2NznNiYnoA4O8fwM6d28nKyiQqqpvzFoY/vvHKzMxk\n4MDmjkqn0xMZGUVeXi4AsbFxABiNBiIjow7/7InV2tAqxqPdapKVdZB//vNqAMxmf3Q6PeXlzd+E\nhodHApCRsZ8tW1JYufInoPlbTJ1Oz4wZs3j22aeora1h7NhLT6qdfH39eP/9t/n226WAgqamJue+\n49WnVCrRaDTMmfMfPDw8KCoqanEunPgbSb1eT23tkQ80tbUtO7I/JCev5PLLL0elUrUov/POu7jp\npn8xadKt9O3bj5CQ0JN6zaJjSV4nxLFJ/3hq/ePYsZcA0j/+YeXKn/jgg3dYtGgR7u7eh8+r/dN5\ntRiNxpN6vcfT5RK7gmoLYfqe6Ny73EsXolP745vDu+/+N3FxH5OcvAKDwcisWf8hNzeHZcu+wuFw\nALS6jTo4OITMzAwaGupRqzXs3ZvO2LGXEhkZyY4dW7nwwlHU1tZw4MABgoODj3qNUxUREUVKSgqX\nXhqGxVJEVVUlnp5eLa4dERHJ2LE9GTv2EsrKSvnmm68pLi4mPX0PTz/9PA0NDVx99WVcfPE4FAoF\nDof9mPW99dbrXHHF3xk2bDjffruM779f7tx3vPr279/HL7+s5s0336e+vt75be2fnegbycjIKHJz\nc6isrMDDQ8e2bVu57rqbWh2XkrKR6dOPdPCbN29i9epV3HPP/Wi1bqjVarkFvjOTfxshOqWzsX/c\nvn0bw4cPkv4R+PHH71i6dAkLFrxBWFgoFksV8fEJLFr0Gg0NDTQ2NpKVdZCoqOiTa+Dj6FLZTX1j\nPQ32emLMISc+WAjR4aKiunHNNf/kpZeeY+LEScyZ8yA7dmzD3d2d0NAwiostRz3PZDJx++3/5t//\nnoi3twkPDw8Axo+/imeeeZLJk2+joaGBiRPvwGRqm9H6m2++lRdemMvy5d/R0NDArFn/Qa1W/+WY\nicyb9wTLli05fKvLJHx9fSktLeHWW6/Hw0PHhAk3olar6dmzF6+//ipBQSF8992yVs8QjBo1mpdf\nfp4PP3wXf/8AysvLjxJT6/pCQ8Pw8PDgtttuQqvV4Ovrd8x2PBa1Ws3UqTOYOXMadrudyy4bj9ns\nz8GDGSxe/Dn33jsbgOzsLMLCwmg4/KVuYmJ/kpNXMHnyRGw2O1dddW2L2dNE5yJpnRCd19nWPz79\n9OPccMNqqqtru3T/OGPGfbz00vMEBATy4IP3odWqSUjoy2233ck110zgrrvuwG63M2nSFNzcznxS\nR4XjjxS/k2uLIe4GbTWzls/npvA7GRjn3wZRnRva6xaCs5m0SWvSJq1Jm7TWVm1iNp/5LSldyZm2\n+cR5qwj01TH3jqFtFNG5QX7HW5M2aU3a5OikXVprizY5Xv/YpRYoL6uvoKFWTUyYt6tDEUIIIToV\nhYzZCSHEWa1LJXY5pRYcjW546k5/YUchhBDinCR5nRBCnNW6VGKXVVKIm0InD+8LIYQQfyE9oxBC\nnN26VGJXWVuP0ePMH0wUQgghzjXynacQQpzdulRid6iqED+dPF8nhBBCtCaZnRBCnM26VGJX1lhE\niCHI1WEIIYQQnY6M2AkhxNmt3daxs9vtzJkzh/T0dLRaLU8++SQRERHO/Z9//jmffvoparWayZMn\nM2rUqPYKxamearqZZA0lITqLTZt+49VXX2bRondxc3OnuNjCzJlTeeGFBZjN/qxY8SNLlnwBgFKp\nJCamB1Om3I1Go+Gaa64gICAQhUJBXV0d48ZdwdVX/6NN4lqzJpmEhF74+Znb5Hptbdmyr1i6dAkq\nlYpbbrmN4cMvaLF/6tRJzp+zs7O49NLLmTx5Grfeej16vQFoXrT2wQcf7dC4RbPO2D+CJHZCdCbS\nP56eE/WPKSkbefPN/6JWqzGZTDz00OO4u7vzxhsLSUnZiEKhYPr0e1usk3c2abfEbsWKFVitVj77\n7DO2bdvGvHnz+O9//wuAxWLhww8/ZPHixTQ0NHD99dczfPhwtFpte4VDo70Jh8NBkLfciilEZzFo\n0FCGDPmdBQteYvr0e3nkkQeYNm0GZrM/Gzas45tvvuaZZ+ZjNBpxOBwsWPAi33+/nPHjrwTgxRdf\nxc3NjcbGRm644RqSki5qkwVWv/jiEyIjH+yUHVdJSTFffvkpb731IVarlSlTbmPQoCEt/n6++uoi\nAPLycnnkkQe45ZbmBWj/vE+4TmfrH4+QzE6IzkL6x1N3Mv3jCy/MY+HCN/Hx8eX111/lm2++pm/f\nRHbv3sWiRe9RUJDP7Nn38P77n7jwlZy+dkvsNm/ezAUXNGfJiYmJ7Nq1y7lvx44d9OvXD61Wi1ar\nJTw8nLS0NPr06dNe4VBWUwV2FYE++narQwhx6iZNuovJk29j9uyZDBw4mEGDmhdI/vLLz5ky5f8w\nGpsX4lQoFEybNvOos9rW19ej1bphMBhpamri6acfIy8vD5vNxoQJNzB69Fj27k1j/vznUKlUaLVa\nZs16CJPJxCOPzKampoaGhnomT76b+vp69u/fy5NPPsJrr73NvHlPcMcdUwgMDHTWl5GxnwUL5qNW\nKyktLWf69Hvp3bsvV199ORERkURERDFhwg08++xcrNYGtFo3Zs16kICAQF5//VXS0nZTW1tLZGRU\nq1GzefOeIDc3x7nt6enF3LnPObf37Emld+++zr+fISFhHDiwj/j4hFbt8sorLzB58jR0Oh2pqbuo\nr69nxoy7sNlsTJp0F7169T6zfzxxWjpb//gHlVISOyE6k7O5f7TbHdTX1zB16sxO1T8uWLAIHx9f\nAGw2G1qtltjYOF54YQEKhYKCgnx8fM48AXaVdkvsqqurMRgMzm2VSkVTUxNqtZrq6mrnmxFAr9dT\nXV193OuZTDrUatVpx+Pp7c5VBRMIDPA67Wucy463in1X1RXb5J7vHyenMr/NrhfmGcQLlz5ywuNu\nuOE65syZw9NPP+Vs96KifBIT4zEYDGzdupUXX3yRxsZGgoKCmD9/PiqVkvvv/z8UCgUZGRlcdNFF\nBAWZ+OijjwgM9OeVV16iurqaq666irFjR/HCC0/z1FNPER8fz4oVK3jzzQVMmzaNyspy3nvvPUpK\nSsjMzOTiiy/lyy8/Zs6cOQQH+/DKK/NbxbtpUz4PP/wfevTowTfffMOqVT+QlHQ+RUWFLF36NSaT\nienTp3Pbbf/iwgsvZMOGDbz77us89thjBAb68fDDH2K327nsssuw22sJCAhwXvuFF549blsplTbM\nZh9nO/n4eKFW21u9X9PS0mhsbODSS0cDEBTkw6RJd3DttdeSmZnJHXfcwQ8//IBa3fbdQFf83TkV\nbd0/wpn3kS/PHIlBp8Fs0p32Nc5V8n5urSu2ifSP50b/+MfPP//8Mzt2bOGBB+7Dza15xvz58+fz\nwQcf8PDDD7fre7w9r91uiZ3BYKCmpsa5bbfbnR8g/rqvpqamRUd2NGVltWcc03XnDcdiqTrj65xr\nzGajtMtfdNU2mT1wxjH3nW6bnOicgoJ83nhjEZMnT2P69Jm88srrqFQqfH3N7NiRTkxMLKGh3Xnx\nxdfIysrkuefmYrFUYbPZeeaZl523mtx77//x0Uefs2tXGgMHDnbWGxYWwY4d6RQUFOLnF4rFUkVU\nVDxpac/h7R3I+PFXM3Xq3TQ1NXHNNROwWKqwWpsoK6s9ZuxarZH581/By8tAaWkFer0ei6UKLy9v\nmprUWCxV7NmTRl7ea7z22usAqNVqqqoayc0tYMqU5lG06uoaCgvLUSqPfJg+0TeSdruK4uJyZ2yl\npRXYbKpWsX722ZdccskVznKDwY/zzhtFcXE1BoMfBoORtLSDBAQE0pba6nfnXP7g2Nb9I5x5H2nU\nKjGbdF3y797xdNW+4Hi6aptI/3hq/aObmxs2mxW12q0T9o8fsXr1Sp555mUqK62AFYAbb7ydq666\njkmTbqVbt3hCQkJP8l/y5LXF78/x+sd2S+z69+9PcnIy48aNY9u2bcTGxjr39enTh5deeomGhgas\nVisHDhxosV8I0TU0Njby8MOzufvumQwbdj7p6Wm8++6b3H77v7nmmn/y2msv88QTz/x/e/cfFVWd\n/3H8OYxC/mBRhFxx5eSxRFJJWcPNX0mEWSAEyo9IUsPF1G+ksSiWaZaS+WNdzTws3/Vg60lbtRZL\nT5aFq0W1rohy3C1J1tzcr0Ek+AN0cJj7/cN1NhwcspJp4PX4a+Ze5n7evA+HF2/unTv2sxslJQea\nvNSkffv2+Pr6cunSJW655RZKS0u4++5w6upqKS8vJyAgAD8/f44d+5xbb72NQ4cO0qtXIOXlx6ir\nq2X58tUBWaOOAAAPu0lEQVRUVVUxffqjDB8+Eg8PD2w22zXrXr16OQsWLObOO0NYunQFp079H3D5\nDexXBAbewkMPTWTgwDs4ceILSkqK+eSTIiorK3juuReorq5m3749GIbR6NjZ2c847VlwcH/y8tZh\nsVi4dOkSJ04cp3fvPg5fd+DA33j44Un25zt3vkl5+TF+85tsqqq+pra2lm7d/JyuJTeG8lFEmuPu\n+XjLLb3ZvDmf8vIvgJ9OPr7yynqOHv2M3/1uHV5eNwFQXPw3/vKXQjIz5+Lp6UW7du2a7KU7uGGD\nXWRkJEVFRSQnJ2MYBjk5OeTn5xMYGEhERASpqamkpKRgGAazZ8+2nwYVkbZj7dpVhIQM4q67RgCQ\nmTmXtLRUQkOHMGLE3VitVubNywQun7m47ba+PP30Ivvrn3zyf+wh4+9/M2PG3I/JZOLFFxczffrl\nG4Y8+uiv6drVl7lzn2bVqmUYhoHZbCY7+xn8/PzJz89j166dtGvXnrS0aQAMGBDC4sULWbVqLatX\nr3R4D8GYMfeTnZ1J9+7+dOnSjTNnahy+t5kzn2DlyqXU19djsVzkiSd+Q48eAWzYsJ709Ml4enoS\nENCTqqqvCQj47nfr7dbNjwkTkpk589fYbDbS02fg5eVFcfHfKC09xJQpvwbg9Olv8PH5782ioqNj\nWbLkWaZPT8NkMjFv3oIbchmmNE/5KCLNcfd89PX1pVevyxl3NVflY2xsPPn5/0vfvv3IzMwAICJi\nDDExcezZ8x7Tpz9KQ4ON+PiE61r3p8RkXD0O/0T9WJf2tMXLB5qjvjhSTxypJ47UE0e6FNM1lJE3\nhnriSD1xpJ40TX1xdKMvxWxTH1AuIiIiIiLSGmmwExERERERcXMa7ERERERERNycBjsRERERERE3\np8FORERERETEzWmwExERERERcXMa7ERERERERNycBjsRERERERE3p8FORERERETEzWmwExERERER\ncXMmwzAMVxchIiIiIiIi35/O2ImIiIiIiLg5DXYiIiIiIiJuToOdiIiIiIiIm9NgJyIiIiIi4uY0\n2ImIiIiIiLg5DXYiIiIiIiJurlUOdjabjQULFpCUlERqaionTpxotH/Lli3Ex8eTmJjInj17XFRl\ny2quJxs2bCAhIYGEhATWrl3roipbVnM9ufI1U6dOZfPmzS6osOU115O9e/eSmJhIYmIizz77LG3l\n01Ka68v69euJj49n/Pjx7N6920VVtrzDhw+TmprqsL2wsJDx48eTlJTEli1bXFCZXIvysWnKSEfK\nSEfKSEfKx2tzSUYardA777xjzJ071zAMwygpKTEee+wx+77KykojOjrasFgsxtmzZ+2PWztnPfnX\nv/5lxMXFGVar1WhoaDCSkpKMTz/91FWlthhnPbli5cqVxoQJE4xNmza1dHku4awn586dM6Kiooxv\nvvnGMAzDyMvLsz9u7Zz15cyZM8bdd99tWCwWo6amxhg9erSrymxReXl5RnR0tJGQkNBoe319vXHv\nvfcaNTU1hsViMeLj443KykoXVSlXUz42TRnpSBnpSBnpSPnYNFdlZKs8Y1dcXMzIkSMBGDRoEEeO\nHLHvKy0tZfDgwXh6euLt7U1gYCCfffaZq0ptMc568vOf/5w//OEPmM1mPDw8sFqteHl5uarUFuOs\nJwC7du3CZDIxatQoV5TnEs56UlJSQt++fXnxxRdJSUnBz88PX19fV5Xaopz1pUOHDgQEBHDhwgUu\nXLiAyWRyVZktKjAwkJdeeslhe3l5OYGBgfj4+ODp6ckvf/lLDhw44IIKpSnKx6YpIx0pIx0pIx0p\nH5vmqoxs96Md6Sfk/PnzdO7c2f7cbDZjtVpp164d58+fx9vb276vU6dOnD9/3hVltihnPWnfvj2+\nvr4YhsGyZcu4/fbb6d27twurbRnOelJWVsaOHTtYs2YNL7/8sgurbFnOelJdXc1f//pXCgoK6Nix\nIw8//DCDBg1q8z8rAD169CAqKoqGhgamTZvmqjJb1H333cfJkycdtrfV37HuQvnYNGWkI2WkI2Wk\nI+Vj01yVka1ysOvcuTO1tbX25zabzf4DdvW+2traRg1urZz1BMBisfDUU0/RqVMnFi5c6IoSW5yz\nnhQUFFBRUcGkSZP497//Tfv27enZs2er/8+ks5506dKFgQMH4u/vD8CQIUP49NNPW31ogfO+7Nu3\nj8rKSt5//30A0tLSCA0NJSQkxCW1ulpb/R3rLpSPTVNGOlJGOlJGOlI+Xp8b/Xu2VV6KGRoayr59\n+wA4dOgQffv2te8LCQmhuLgYi8XCuXPnKC8vb7S/tXLWE8MwmDFjBkFBQTz33HOYzWZXldminPVk\nzpw5bN26lY0bNxIXF8fkyZNbfWCB854MGDCAsrIyTp8+jdVq5fDhw9x6662uKrVFOeuLj48PN910\nE56ennh5eeHt7c3Zs2ddVarL9enThxMnTlBTU0N9fT0HDhxg8ODBri5L/kP52DRlpCNlpCNlpCPl\n4/W50RnZKs/YRUZGUlRURHJyMoZhkJOTQ35+PoGBgURERJCamkpKSgqGYTB79uw2ca28s57YbDb2\n799PfX09H3zwAQBPPvlkq/9jrLmfk7aouZ5kZmYydepUAMaOHdtm/uhrri8fffQRiYmJeHh4EBoa\nyvDhw11dcot76623qKurIykpiezsbNLS0jAMg/Hjx9O9e3dXlyf/oXxsmjLSkTLSkTLSkfLxu2mp\njDQZRhu4F6uIiIiIiEgr1iovxRQREREREWlLNNiJiIiIiIi4OQ12IiIiIiIibk6DnYiIiIiIiJvT\nYCciIiIiIuLmWuXHHYh828mTJxk7dix9+vRptD03N5cePXo0+ZqXXnoJgMcff/x7r/vGG2+wdOlS\n+xoXL14kLCyMhQsXNvrg2+9i9erVDBgwwH478o0bNwIQGxvL9u3bv3eNAKmpqXz11Vd07NgRgPPn\nz9OrVy9WrFiBn5/fNV+3ZcsWOnbsSHR09A9aX0RE2q6rM9pms1FbW8uDDz5IRkbGj7LGtzM9KCiI\no0eP/ijHFfmp0WAnbcLNN9/8gweg7+Oee+5h6dKlADQ0NJCcnMy2bdtITk6+ruM88cQT9sf79++3\nP/6xvqfFixczdOhQ4HKoZmRkkJ+fT1ZW1jVfc/DgQcLCwn6U9UVEpO26OqMrKiq47777iIqKcvin\nrIhcmwY7adPKysp4/vnnqaur4/Tp06Snp/PQQw/Z91+6dImnnnqKzz//HICUlBQSExOpqqpiwYIF\nfPXVV5hMJjIzMxk2bJjTtcxmM0OGDLEf6/XXXyc/Px+TyUT//v155pln8PT0bHK97OxswsLC+Mc/\n/gFAQkICW7duJSgoiL///e+MHj2agoIC/Pz8qKmpITo6mj179vDxxx+zZs0arFYrv/jFL3j++efp\n2rWr0zrr6uqorq4mJCQEgLfffpv8/HwuXrxIfX09OTk5XLx4kcLCQj755BP8/f0JDg6+7n6IiIg0\n5euvv8YwDDp16kReXh5vv/02DQ0NjBgxgqysLEwmExs2bGDz5s2YzWbCw8PJyspqNtNFWjsNdtIm\nVFZWEhsba38+btw4pk6dytatW5kxYwZ33XUXX375JTExMY1CoKSkhDNnzlBQUEBFRQUrV64kMTGR\nJUuWMH78eCIiIqisrCQlJYWCggI6d+58zRqqq6v58MMPSU9P5+jRo+Tm5rJlyxa6du3KokWLWLt2\nLeHh4U2ud8X8+fPZuHEjW7dutW9r164dY8eOZdeuXUycOJF3332XyMhIzp07x8qVK/njH/+Ij48P\nr732GitWrGDJkiUOtc2fP58OHTpw+vRpfHx8eOCBB5g8eTI2m43XXnuN3NxcfH192bZtG3l5eeTm\n5nLPPfcQFhbGyJEjmT179nX3Q0REBP6b0RaLherqagYOHMjatWspKyvjyJEjbNu2DZPJRFZWFm++\n+Sa9e/dm06ZNvP7663To0IGpU6dy5MgRtm/f7jTTRVo7DXbSJlzrUszs7Gw++OADfv/731NWVkZd\nXV2j/bfddhvHjx8nLS2NUaNGMWfOHAA++ugj/vnPf7JmzRoArFYrX375JcHBwY1eX1hYSGxsLIZh\nYBgGkZGRREdH8+qrrxIeHm4/e5aUlMS8efNIT09vcr3mxMTE8MILLzBx4kR27NjB7NmzOXz4MKdO\nneKRRx4BLl9i6ePj0+Trr1yKefDgQTIyMoiMjMTT0xOAl19+mcLCQo4fP87+/fvx8HC859J37YeI\niMjVrmS0zWZj6dKllJeXM3z4cJYvX05paSnx8fHA5feqBwQEUFVVRXh4ON7e3gBs2LABgODgYKeZ\nLtLaabCTNm3WrFn87Gc/Izw8nAceeIAdO3Y02t+1a1d27txJUVERe/fuJS4ujp07d2Kz2XjllVfo\n0qULcPm/jd26dXM4/rffY/dtNput0XPDMLBarddcrzkhISGcOXOG0tJSKioqGDx4MO+99x6hoaHk\n5uYCYLFYqK2tdXqc0NBQUlNTyczM5M9//jMWi4UJEyYQExPDnXfeSVBQEK+++mqT38936YeIiMi1\neHh4MGfOHB588EHWr19PQ0MDkyZNYsqUKQCcPXsWs9lsP4N3RUVFBR06dODpp592mukirZ0+7kDa\ntKKiIjIyMrj33nvZt28fcPkmJ1e8//77ZGVlMXr0aObPn0/Hjh05deoUv/rVr9i0aRMAx44dY9y4\ncVy4cOE7rxsWFkZhYSE1NTXA5TtMDh069JrrfZvZbMZqtTocc9y4cSxcuJCoqCgA7rjjDg4dOsTx\n48cBWLduHcuWLWu2tilTplBbW8uf/vQnvvjiC0wmE4899hhDhw5l9+7d9v6YzWb74x/aDxEREbj8\n9oI5c+awbt06br/9drZv305tbS1Wq5WZM2fyzjvvMGTIEPbu3WvfnpmZyZEjR5rNdJHWTmfspE17\n/PHHSUlJwcvLi379+tGzZ09Onjxp3z9q1CjeffddoqKi8PLyIiYmhqCgIObPn8+CBQsYN24cAMuW\nLbuu95P169ePadOmkZqayqVLl+jfvz+LFi3Cy8uryfW+LSIigtjYWN54441G22NiYli9ejWrVq0C\nwN/fn5ycHGbNmoXNZqN79+4sX7682do8PT2ZNWsWOTk57N69m+DgYO6//35MJhMjRoyguLgYgGHD\nhvHb3/4Wb2/vH9wPERGRK0aNGsXgwYM5cOAAY8aMITExkYaGBkaOHElcXBwmk4mJEyeSnJyMzWYj\nMjKSYcOGNZvpIq2dyTAMw9VFiIiIiIiIyPenSzFFRERERETcnAY7ERERERERN6fBTkRERERExM1p\nsBMREREREXFzGuxERERERETcnAY7ERERERERN6fBTkRERERExM1psBMREREREXFz/w8DdnFTzRgX\nSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e89cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax1.set_xlim([-0.05,1.05])\n",
    "ax1.set_ylim([-0.05,1.05])\n",
    "ax1.set_xlabel('False Positive Rate')\n",
    "ax1.set_ylabel('True Positive Rate')\n",
    "ax1.set_title('ROC Curve')\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2.set_xlim([-0.05,1.05])\n",
    "ax2.set_ylim([-0.05,1.05])\n",
    "ax2.set_xlabel('Recall')\n",
    "ax2.set_ylabel('Precision')\n",
    "ax2.set_title('PR Curve')\n",
    "\n",
    "# ax1.plot(fpr_lr, tpr_lr, lw=1, label='LR: area = %0.2f'%roc_auc_lr)\n",
    "ax1.plot(fpr_rf, tpr_rf, lw=1, label='Random Forest: area = %0.2f'%roc_auc_rf)\n",
    "ax1.plot(fpr_xgb, tpr_xgb, lw=1, label='XGBoost: area = %0.2f'%roc_auc_xgb)\n",
    "\n",
    "# ax2.plot(r_lr, p_lr, lw=1, label='LR: area = %0.2f'%average_p_lr)\n",
    "ax2.plot(r_rf, p_rf, lw=1, label='Random Forest: area = %0.2f'%average_p_rf)\n",
    "ax2.plot(r_xgb, p_xgb, lw=1, label='XGBoost: area = %0.2f'%average_p_xgb)\n",
    "\n",
    "ax1.legend(loc='lower right')    \n",
    "ax2.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Performance comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wanting.Wang@ibm.com/.local/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "# calculate accuracy for logistic regression\n",
    "# score_lr = final_lr.score(X_test, y_test)\n",
    "\n",
    "# calculate accuracy for random forest\n",
    "score_rf = final_rf.score(X_test, Y_test)\n",
    "\n",
    "# calculate accuracy for xgboost\n",
    "score_xgb = final_xgb.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Binary Accuracy</th>\n",
       "      <th>AUC of ROC</th>\n",
       "      <th>AUC of PR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.795734</td>\n",
       "      <td>0.725910</td>\n",
       "      <td>0.197347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.919207</td>\n",
       "      <td>0.747373</td>\n",
       "      <td>0.228378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Binary Accuracy  AUC of ROC  AUC of PR\n",
       "0  Logistic Regression         0.000000    0.000000   0.000000\n",
       "1        Random Forest         0.795734    0.725910   0.197347\n",
       "2              XGBoost         0.919207    0.747373   0.228378"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_lr = 0\n",
    "roc_auc_lr = 0\n",
    "average_p_lr = 0\n",
    "\n",
    "result = {'Model':['Logistic Regression', 'Random Forest', 'XGBoost'],\n",
    "          'Binary Accuracy':[score_lr, score_rf, score_xgb],\n",
    "          'AUC of ROC':[roc_auc_lr, roc_auc_rf, roc_auc_xgb],\n",
    "          'AUC of PR':[average_p_lr, average_p_rf, average_p_xgb]}\n",
    "pd.DataFrame(result,columns=['Model', 'Binary Accuracy', 'AUC of ROC', 'AUC of PR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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